Joachim Weickert

Publications

Joachim Weickert: Publications

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Book – Edited Books – Book Chapters – Edited Special Journal Issues – Journal Papers – Conference Papers – Patent – Theses – Unpublished Technical Reports

1. J. Weickert:
   Anisotropic Diffusion in Image Processing.
   Teubner, Stuttgart, 1998.
   [More information]

8. J. Weickert, M. Hein, B. Schiele (Eds.):
   Pattern Recognition.
   Lecture Notes in Computer Science, Vol. 8142, Springer, Berlin, 2013.
7. D. H. Laidlaw, J. Weickert (Eds.):
   Visualization and Processing of Tensor Fields: Advances and Perspectives.
   Springer, Berlin, 2009.
6. H. P. A. Lensch, B. Rosenhahn, H.-P. Seidel, P. Slusallek, J. Weickert (Eds.):
   Vision, Modeling, and Visualization 2007.
   MPI-I Saarbrücken and AKA, Berlin, Nov. 2007.
5. J. Weickert, H. Hagen (Eds.):
   Visualization and Processing of Tensor Fields.
   Springer, Berlin, 2006.
4. R. Klette, R. Kozera, L. Noakes, J. Weickert (Eds.):
   Geometric Properties from Incomplete Data.
   Springer, Dordrecht, 2006.
3. R. Kimmel, N. Sochen, J. Weickert (Eds.):
   Scale-Space and PDE Methods in Computer Vision.
   Lecture Notes in Computer Science, Vol. 3459, Springer, Berlin, 2005.
2. J. Sporring, W. Niessen, J. Weickert (Eds.),
   Proceedings of the International Workshop on Growth and Motion in 3D Medical Images.
   3D-Lab, University of Copenhagen, Denmark, 2002. ISBN 87-988979-1-8.
   Selected and revised papers of this volume have appeared in a Special Issue of IEEE Transactions on Medical Imaging (Vol. 22, No. 6, 2003).
1. M. Nielsen, P. Johansen, O.F. Olsen, J. Weickert (Eds.),
   Scale-Space Theories in Computer Vision.
   Lecture Notes in Computer Science, Vol. 1682, Springer, Berlin, 1999.

25. L. Hoeltgen, M. Mainberger, S. Hoffmann, J. Weickert, C. H. Tang, S. Setzer, D. Johannsen, F. Neumann, B. Doerr:
    Optimising spatial and tonal data for PDE-based inpainting.
    In M. Bergounioux, G. Peyré, C. Schnörr, J.-P. Caillau, T. Haberkorn (Eds.): Variational Methods in Imaging and Geometric Control. Pages 35-83, De Gruyter, Berlin, 2017.
    Also available as arXiv:1506.04566 [cs.CV], June 2015.
24. R. Moreno, L. Pizarro, B. Burgeth, J. Weickert, M. A. Garcia, D. Puig:
    Adaptation of tensor voting to image structure estimation.
    In D. H. Laidlaw, A. Vilanova (Eds.): New Developments in the Visualization and Processing of Tensor Fields. Pages 29-50, Springer, Berlin, 2012.
    Also available as Linköping University Postprint, 2012.

23. B. Burgeth, L. Pizarro, S. Didas, J. Weickert:
    3D coherence-enhancing diffusion filtering for matrix fields.
    In L. Florack, R. Duits, G. Jongbloed, M.-C. van Lieshout, L. Davies (Eds.): Mathematical Methods for Signal and Image Analysis and Representation. Pages 49-63, Springer, Berlin, 2012.
22. B. Burgeth, M. Breuß, S. Didas, J. Weickert:
    PDE-based morphology for matrix fields: Numerical solution schemes.
    In S. Aja-Fernandez, R. de Luis-Garcia, D. Tao, X. Li (Eds.): Tensors in Image Processing and Computer Vision. Pages 59-77, Springer, London, 2009.
    Revised version of Technical Report No. 220, Department of Mathematics, Saarland University, Saarbrücken, Germany, September 2008.

21. S. Barbieri, M. Welk, J. Weickert:
    A variational approach to the registration of tensor-valued images.
    In S. Aja-Fernandez, R. de Luis-Garcia, D. Tao, X. Li (Eds.): Tensors in Image Processing and Computer Vision. Pages 263-279, Springer, London, 2009.
    Revised version of Technical Report No. 221, Department of Mathematics, Saarland University, Saarbrücken, Germany, September 2008.

20. T. Schultz, J. Weickert, H.-P. Seidel:
    A higher-order structure tensor.
    In D. H. Laidlaw, J. Weickert (Eds.): Visualization and Processing of Tensor Fields: Advances and Perspectives. Pages 263-279, Springer, Berlin, 2009.
    Revised version of Research Report MPI-I-2007-4-005, Max-Planck-Institut für Informatik, Saarbrücken, Germany, July 2007.

19. B. Burgeth, S. Didas, J. Weickert:
    A general structure tensor concept and coherence-enhancing diffusion filtering for matrix fields.
    In D. H. Laidlaw, J. Weickert (Eds.): Visualization and Processing of Tensor Fields: Advances and Perspectives. Pages 305-323, Springer, Berlin,, 2009.
    Revised version of Technical Report No. 197, Department of Mathematics, Saarland University, Saarbrücken, Germany, July 2007.

18. R. Dahlhaus, J. Franke, J. Polzehl, V. Spokoiny, G. Steidl, J. Weickert, A. Berdychevski, S. Didas, S. Halim, P. Mrázek, S. S. Rao, J. Tadjuidje:
    Structural adaptive smoothing procedures.
    In R. Dahlhaus, J. Kurths, P. Maass, J. Timmer (Eds.): Mathematical Methods in Time Series Analysis and Digital Image Processing. Pages 325-339, Springer, Berlin, 2008.

17. B. Burgeth, J. Weickert, S. Tari:
    Minimally stochastic schemes for singular diffusion equations.
    In X.-C. Tai, K.-A. Lie, T. F. Chan, S. Osher (Eds.): Image Processing Based on Partial Differential Equations. Pages 325-339, Springer, Berlin, 2007.

16. J. Weickert, A. Bruhn, T. Brox, N. Papenberg:
    A survey on variational optic flow methods for small displacements.
    In O. Scherzer (Ed.): Mathematical Models for Registration and Applications to Medical Imaging. Pages 103-136, Springer, Berlin, 2006.
    Also available as Technical Report No. 152, Department of Mathematics, Saarland University, Saarbrücken, Germany, September 2005.

15. T. Brox, R. van den Boomgaard, F. Lauze, J. van de Weijer, J. Weickert, P. Mrázek, P. Kornprobst:
    Adaptive structure tensors and their applications.
    In J. Weickert, H. Hagen (Eds.): Visualization and Processing of Tensor Fields. Pages 17-47, Springer, Berlin, 2006.
    Revised version of Technical Report No. 141, Department of Mathematics, Saarland University, Saarbrücken, Germany, 2005.

14. B. Burgeth, M. Welk, C. Feddern, J. Weickert:
    Mathematical morphology on tensor data using the Loewner ordering.
    In J. Weickert, H. Hagen (Eds.): Visualization and Processing of Tensor Fields. Pages 357-367, Springer, Berlin, 2006.
    Revised version of Technical Report No. 160, Department of Mathematics, Saarland University, Saarbrücken, Germany, December 2005.

13. J. Weickert, M. Welk:
    Tensor field interpolation with PDEs.
    In J. Weickert, H. Hagen (Eds.): Visualization and Processing of Tensor Fields. Pages 315-325, Springer, Berlin, 2006.
    Revised version of Technical Report No. 142, Department of Mathematics, Saarland University, Saarbrücken, Germany, 2005.

12. J. Weickert, C. Feddern, M. Welk, B. Burgeth, T. Brox:
    PDEs for tensor image processing.
    In J. Weickert, H. Hagen (Eds.): Visualization and Processing of Tensor Fields. Pages 399-414, Springer, Berlin, 2006.
    Revised version of Technical Report No. 143, Department of Mathematics, Saarland University, Saarbrücken, Germany, 2005.

11. M. Welk, C. Feddern, B. Burgeth, J. Weickert:
    Tensor median filtering and M-smoothing.
    In J. Weickert, H. Hagen (Eds.): Visualization and Processing of Tensor Fields. Pages 345-356, Springer, Berlin, 2006.

10. J. Weickert, G. Steidl, P. Mrázek, M. Welk, T. Brox:
    Diffusion filters and wavelets: What can they learn from each other?
    In N. Paragios, Y. Chen, O. Faugeras (Eds.): Handbook of Mathematical Models in Computer Vision. Pages 3-16, Springer, New York, 2006.
    Also available as Preprint No. 77, DFG Priority Programme 1114, University of Bremen, Germany, January 2005.

9. A. Bruhn, J. Weickert:
   A confidence measure for variational optic flow methods.
   In R. Klette, R. Kozera, L. Noakes, J. Weickert (Eds.): Geometric Properties from Incomplete Data. Pages 283-297, Springer, Dordrecht, 2006.
   Revised version of Technical Report No. 106, Department of Mathematics, Saarland University, Saarbrücken, Germany, July 2004.

8. P. Mrázek, J. Weickert, A. Bruhn:
   On robust estimation and smoothing with spatial and tonal kernels.
   In R. Klette, R. Kozera, L. Noakes, J. Weickert (Eds.): Geometric Properties from Incomplete Data. Pages 335-352, Springer, Dordrecht, 2006.
   Revised version of Preprint No. 51, DFG Priority Programme 1114, University of Bremen, Germany, June 2004.

7. J. Weickert, G. Kühne:
   Fast methods for implicit active contour models,
   In S. Osher, N. Paragios (Eds.): Geometric Level Set Methods in Imaging, Vision and Graphics. Pages 43-58, Springer, New York, 2003.

6. W. J. Niessen, K. L. Vincken, J. Weickert, M. A. Viergever:
   Multiscale segmentation of volumetric MR brain images.
   In H. Yan (Ed.): Signal Processing for Magnetic Resonance Imaging and Spectroscopy. Pages 209-238, Marcel Dekker, New York, 2002.

5. J. Weickert:
   Design of nonlinear diffusion filters.
   In B. Jähne, H. Haußecker (Eds.): Computer Vision and Applications. Pages 439-458, Academic Press, San Diego, 2000.

4. J. Weickert:
   Nonlinear diffusion filtering.
   In B. Jähne, H. Haußecker, P. Geißler (Eds.): Handbook on Computer Vision and Applications, Vol. 2: Signal Processing and Pattern Recognition. Pages 423-450, Academic Press, San Diego, 1999.
   [Abstract]

3. J. Weickert, S. Ishikawa, A. Imiya:
   On the history of Gaussian scale-space axiomatics.
   In J. Sporring, M. Nielsen, L. Florack, P. Johansen (Eds.): Gaussian Scale-Space Theory. Pages 45-59, Kluwer, Dordrecht, 1997.
   [Abstract]

2. J. Weickert:
   Nonlinear diffusion scale-spaces.
   In J. Sporring, M. Nielsen, L. Florack, P. Johansen (Eds.): Gaussian Scale-Space Theory. Pages 221-234, Kluwer, Dordrecht, 1997.
   [Abstract]

1. H. Neunzert, B. Claus, K. Rjasanowa, R. Rösch, J. Weickert:
   Mathematische Werkzeuge in der Bildverarbeitung zur Qualitätsbestimmung von Oberflächen.
   In K.-H. Hoffmann, W. Jäger, T. Lohmann, H. Schunck (Eds.): Mathematik - Schlüsseltechnologie für die Zukunft. Pages 449-462, Springer, Berlin, 1997.
   [Abstract]

5. G. Peyré, L. D. Cohen, J. Weickert (Eds.):
   Mathematics and Image Analysis.
   Special Issue of Journal of Mathematical Imaging and Vision, Vol. 41, No. 1-2, Sept. 2011.

4. A. Heyden, K. Mørken, X. Tai, J. Weickert (Eds.):
   Scale Space and Variational Methods in Computer Vision.
   Special Issue of International Journal of Computer Vision, Vol. 92, No. 3, May 2011.
3. A. Heyden, K. Mørken, X. Tai, J. Weickert (Eds.):
   Scale Space and Variational Methods in Computer Vision.
   Special Issue of International Journal of Computer Vision, Vol. 92, No. 2, April 2011.
2. R. Kimmel, N. Sochen, J. Weickert (Eds.):
   Scale Space and PDE Methods in Computer Vision.
   Special Issue of International Journal of Computer Vision, Vol. 70, No. 3, Dec. 2006.
1. J. Sporring, W. Niessen, J. Weickert (Eds.):
   Growth and Motion in Three-Dimensional Medical Images.
   Special Issue of IEEE Transactions on Medical Imaging, Vol. 22, No. 6, June 2003.

115. N. Kämper, V. Chizhov, J. Weickert:
     Efficient parallel data optimization for homogeneous diffusion inpainting of 4K images.
     To appear in SIAM Journal on Imaging Sciences.
     Also available as arXiv:2401.06747 [eess.IV], revised August 2024.
114. K. Schaefer, J. Weickert:
     Regularised diffusion-shock inpainting.
     Journal of Mathematical Imaging and Vision, Vol. 66, 447-463, April 2024.
     Invited Paper.
     
113. P. Peter, K. Schrader, T. Alt, J. Weickert:
     Deep spatial and tonal optimisation for homogeneous diffusion inpainting.
     Pattern Analysis and Applications, Vol. 26, 1585-1600, November 2023.
     Invited Paper.
     
112. T. Alt, K. Schrader, M. Augustin, P. Peter, J. Weickert:
     Connections between numerical algorithms for PDEs and neural networks.
     Journal of Mathematical Imaging and Vision, Vol. 65, 185-208, 2023.
     Invited Paper.
     
111. T. Alt, K. Schrader, J. Weickert, P. Peter, M. Augustin:
     Designing rotationally invariant neural networks from PDEs and variational methods.
     Research in the Mathematical Sciences, Vol. 9, Article 52, August 2022.
     

110. R. M. K. Mohideen, P. Peter, J. Weickert:
     A systematic evaluation of coding strategies for sparse binary images.
     Signal Processing: Image Communication, Vol. 99, Article 116424, November 2021.
     Also available as arXiv:2010.13634 [eess.IV], revised July 2021.
109. V. Daropoulos, M. Augustin, J. Weickert:
     Sparse inpainting with smoothed particle hydrodynamics.
     SIAM Journal on Imaging Sciences, Vol. 14, No. 4, 1669-1704, November 2021.
     Also available as arXiv:2011.11289 [eess.IV], revised August 2021.
108. K. Bodduna, J. Weickert:
     Removing multi-frame Gaussian noise by combining patch-based filters with optical flow.
     Journal of Electronic Imaging, Vol. 30, No. 3, Article 033031, June 2021.
     Also available as arXiv:2001.08058 [eess.IV], revised May 2021.
107. M. Bildhauer, M. Cárdenas, M. Fuchs, J. Weickert:
     Existence theory for the EED inpainting problem.
     St. Petersburg Mathematical Journal, Vol. 32, No. 3, 481-497, May 2021.
     Invited Paper.
     Also available as arXiv:1906.04628v2 [math.AP], September 2019.
106. M. Welk, J. Weickert:
     PDE evolutions for M-smoothers in one, two, and three dimensions.
     Journal of Mathematical Imaging and Vision, Vol. 42, No. 2, 157-185, February 2021.
     Invited Paper.
     Also available as arXiv:2007.13191 [eess.IV], July 2020.
105. S. Müller, J. Weickert, N. Graf:
     Robustness of brain tumor segmentation.
     Journal of Medical Imaging, Vol. 7, No. 6, Article 064006, December 2020
     Also available as arXiv:1912.11312 [eess.IV], revised December 2020.
104. L. Bergerhoff, M. Cárdenas, J. Weickert, M. Welk:
     Stable backward diffusion models that minimise convex energies.
     Journal of Mathematical Imaging and Vision, Vol. 62, No. 6-7, 941-960, July 2020.
     Invited Paper.
     
103. M. Fuchs, J. Weickert:
     Iterative TV-regularization of grey-scale images.
     Journal of Mathematical Sciences, Vol. 242, No. 2, 323-336, October 2019.
     Invited Paper.
     Also available as Technical Report No. 402, Department of Mathematics, Saarland University, Saarbrücken, Germany, April 2019.
102. S. Müller, I. Farag, J. Weickert, Y. Braun, J. Dobberstein, A. Lollert, A. Hötker, N. Graf:
     Benchmarking Wilms' tumor in multi-sequence MRI data: Why does current clinical practice fail? Which popular segmentation algorithms perform well?
     Journal of Medical Imaging, Vol. 6, No. 3, Article 034001, July 2019.
     Also available as Technical Report No. 393, Department of Mathematics, Saarland University, Saarbrücken, Germany, revised May 2019.
101. S. Parisotto, L. Calatroni, M. Caliari, C.-B. Schönlieb, J. Weickert:
     Anisotropic osmosis filtering for shadow removal in images.
     Inverse Problems, Vol. 35, No. 5, Article 054001, April 2019.
     Revised version of arXiv:1809.06298 [math.AP], September 2018.
100. M. Welk, J. Weickert, G. Gilboa:
     A discrete theory and efficient algorithms for forward-and-backward diffusion filtering.
     Journal of Mathematical Imaging and Vision, Vol. 60, No. 9, 1399-1426, Nov. 2018.
     Invited Paper.
     Also available as Preprint ni17005, Isaac Newton Institute for Mathematical Sciences, Cambridge, UK, revised September 2018.

99. M. Fuchs, J. Müller, C. Tietz, J. Weickert:
    Convex regularization of multi-channel images based on variants of the TV-model.
    Complex Variables and Elliptic Equations, Vol. 63, No. 7-8, 976-995, 2018.
    Invited Paper.
    Also available as Technical Report No. 388, Department of Mathematics, Saarland University, Saarbrücken, Germany, June 2017.
98. N. Amrani, J. Serra-Sagrista, P. Peter, J. Weickert:
    Diffusion-based inpainting for coding remote-sensing data.
    IEEE Geoscience and Remote Sensing Letters, Vol. 14, No. 8, 1203-1207, August 2017.
    Also available as Technical Report, Universitat Autonoma de Barcelona, Spain, March 2017, http://ddd.uab.cat/record/174184.
97. M. Bildhauer, M. Fuchs, J. Weickert:
    An alternative approach towards the higher order denoising of images. Analytical aspects.
    Journal of Mathematical Sciences, Vol. 224, No. 3, 414-441, July 2017.
    Invited Paper.
    Revised version of Technical Report No. 374, Department of Mathematics, Saarland University, Saarbrücken, Germany, January 2016.
96. P. Peter, L. Kaufhold, J. Weickert:
    Turning diffusion-based image colorization into efficient color compression.
    IEEE Transactions on Image Processing, Vol. 26, No. 2, 860-869, February 2017.
    Revised version of Technical Report No. 370, Department of Mathematics, Saarland University, Saarbrücken, Germany, December 2015.
95. N. Persch, C. Schroers, S. Setzer, J. Weickert:
    Physically inspired depth-from-defocus.
    Image and Vision Computing, Vol. 57, No. 1, 114-129, January 2017.
    Also available as Technical Report No. 355 (revised), Department of Mathematics, Saarland University, Saarbrücken, Germany, June 2016.
94. M. Bildhauer, M. Fuchs, J. Weickert:
    Denoising and inpainting of images using TV-type energies: Theoretical and computational aspects.
    Journal of Mathematical Sciences, Vol. 219, No. 6, 899-910, December 2016.
    Also avalailable as Technical Report No. 380, Department of Mathematics, Saarland University, Saarbrücken, Germany, June 2016.
93. M. Schmidt, J. Weickert:
    Morphological counterparts of linear shift-invariant scale-spaces.
    Journal of Mathematical Imaging and Vision, Vol. 56, No. 2, 352-366, October 2016.
    Invited Paper.
    Also available as Technical Report No. 365 (revised), Department of Mathematics, Saarland University, Saarbrücken, Germany, February 2016.
92. S. F. Huckemann, K.-R. Kim, A. Munk, F. Rehfeld, M. Sommerfeld, J. Weickert, C. Wollnik:
    The circular SiZer, inferred persistence of shape parameters and application to stem cell stress fibre structures.
    Bernoulli Journal, Vol. 22, No. 4, 2113-2142, 2016.
    Also availabe as arXiv:1404.3300 [stat.ME], April 2014.
91. P. Peter, S. Hoffmann, F. Nedwed, L. Hoeltgen, J. Weickert:
    Evaluating the true potential of diffusion-based inpainting in a compression context.
    Signal Processing: Image Communication, Vol. 46, 40-53, August 2016.
    Revised version of Technical Report No. 373, Department of Mathematics, Saarland University, Saarbrücken, Germany, January 2016.
90. G. Plonka, S. Hoffmann, J. Weickert:
    Pseudo-inverses of difference matrices and their application to sparse signal approximation.
    Linear Algebra and its Applications, Vol. 503, 26-47, August 2016.
    Revised version of arXiv:1504.04266 [math.NA], April 2015.
89. J. Weickert, S. Grewenig, C. Schroers, A. Bruhn:
    Cyclic schemes for PDE-based image analysis.
    International Journal of Computer Vision, Vol. 118, No. 3, 275-299, July 2016.
    Also available as Technical Report No. 327 (revised), Department of Mathematics, Saarland University, Saarbrücken, Germany, April 2015.
88. D. Hafner, J. Weickert:
    Variational image fusion with optimal local contrast.
    Computer Graphics Forum, Vol. 35, No. 1, 100-112, February 2016.
    Also available as Technical Report No. 360, Department of Mathematics, Saarland University, Saarbrücken, Germany, April 2015.
    See also: Supplementary Material Webpage.
87. M. Krause, R. M. Alles, B. Burgeth, J. Weickert:
    Fast retinal vessel analysis.
    Journal of Real-Time Image Processing, Vol. 11, No. 2, 413-422, February 2016.
    Also available as Technical Report No. 320, Department of Mathematics, Saarland University, Saarbrücken, Germany, Dec. 2012 (revised Febr. 2013).
86. M. Boshtayeva, D. Hafner, J. Weickert:
    A focus fusion framework with anisotropic depth map smoothing.
    Pattern Recognition, Vol. 48, No. 11, 3310-3323, November 2015.
    Invited Paper.
    Revised version of Technical Report No. 343, Department of Mathematics, Saarland University, Saarbrücken, Germany, February 2014.
85. P. Peter, C. Schmaltz, N. Mach, M. Mainberger, J. Weickert:
    Beyond pure quality: Progressive modes, region of interest coding, and real time video decoding for PDE-based image compression.
    Journal of Visual Communication and Image Representation, Vol. 31, 253-265, August 2015.
    Revised version of Technical Report No. 354, Department of Mathematics, Saarland University, Saarbrücken, Germany, January 2015.
84. O. Demetz, D. Hafner, J. Weickert:
    Morphologically invariant matching of structures with the complete rank transform.
    International Journal of Computer Vision, Vol. 113, No. 3, 220-232, July 2015.
    Invited Paper.
    Revised version of Technical Report No. 348, Department of Mathematics, Saarland University, Saarbrücken, Germany, May 2014.
83. D. Hafner, O. Demetz, J. Weickert, M. Reißel:
    Mathematical foundations and generalisations of the census transform for robust optic flow computation.
    Journal of Mathematical Imaging and Vision, Vol. 52, No. 1, 71-86, May 2015.
    Revised version of Technical Report No. 337, Department of Mathematics, Saarland University, Saarbrücken, Germany, Oct. 2013.
82. C. Schroers, S. Setzer, J. Weickert:
    A variational taxonomy for surface reconstruction from oriented points.
    Computer Graphics Forum, Vol. 33, No. 5, 195-204, August 2014.
    Revised version of Technical Report No. 349, Department of Mathematics, Saarland University, Saarbrücken, Germany, June 2014.

81. C. Schmaltz, P. Peter, M. Mainberger, F. Ebel, J. Weickert, A. Bruhn:
    Understanding, optimising, and extending data compression with anisotropic diffusion.
    International Journal of Computer Vision, Vol. 108, No. 3, 222-240, July 2014.
    Revised version of Technical Report No. 329, Department of Mathematics, Saarland University, Saarbrücken, Germany, March 2013.
80. P. Gwosdek, C. Schmaltz, J. Weickert, T. Teuber:
    Fast electrostatic halftoning.
    Journal of Real-Time Image Processing, Vol. 9, No. 2, 379-392, June 2014.
    Revised version of Technical Report No. 295, Department of Mathematics, Saarland University, Saarbrücken, Germany, June 2011.
79. N. Persch, A. Elhayek, M. Welk, A. Bruhn, S. Grewenig, K. Böse, A. Kraegeloh, J. Weickert:
    Enhancing 3-D cell structures in confocal and STED microscopy: A joint model for interpolation, deblurring and anisotropic smoothing.
    Measurement Science and Technology, Vol. 24, No. 12, 125703, Dec. 2013.
    Revised version of Technical Report No. 321, Department of Mathematics, Saarland University, Saarbrücken, Germany, Jan. 2013.
78. C. Schmaltz, P. Gwosdek, J. Weickert:
    Multi-class anisotropic electrostatic halftoning.
    Computer Graphics Forum, Vol. 31, No. 6, 1924-1935, Sept. 2012.
    Revised version of Technical Report No. 301, Department of Mathematics, Saarland University, Saarbrücken, Germany, Oct. 2011.
    See also: Supplementary Material Webpage.
77. C. Schmaltz, B. Rosenhahn, T. Brox, J. Weickert:
    Region based pose tracking with occlusions using 3D models.
    Machine Vision and Applications, Vol. 23, No. 3, 557-577, May 2012.
    Revised version of Technical Report No. 277, Department of Mathematics, Saarland University, Saarbrücken, Germany, October 2010.
76. L. Valgaerts, A. Bruhn, M. Mainberger, J. Weickert:
    Dense versus sparse approaches for estimating the fundamental matrix.
    International Journal of Computer Vision, Vol. 96, No. 2, 212-234, Jan. 2012.
    Revised version of Technical Report No. 263, Department of Mathematics, Saarland University, Saarbrücken, Germany, October 2010.
75. R. Moreno, M. A. Garcia, D. Puig, L. Pizarro, B. Burgeth, J. Weickert:
    On improving the efficiency of tensor voting.
    IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33, No. 1, 2215-2228, Nov. 2011.
    Also available as Linköping University Postprint.
74. M. Mainberger, A. Bruhn, J. Weickert, S. Forchhammer:
    Edge-based compression of cartoon-like images with homogeneous diffusion.
    Pattern Recognition, Vol. 44, No. 9, 1859-1873, Sept. 2011.
    Invited Paper.
    Also available as Technical Report No. 269, Department of Mathematics, Saarland University, Saarbrücken, Germany, August 2010.
73. H. Zimmer, A. Bruhn, J. Weickert:
    Optic flow in harmony.
    International Journal of Computer Vision, Vol. 93, No. 3, 368-388, July 2011.
    Revised version of Technical Report No. 272, Department of Mathematics, Saarland University, Saarbrücken, Germany, August 2010.
72. M. Breuß, J. Weickert:
    Highly accurate schemes for PDE-based morphology with general structuring elements.
    International Journal of Computer Vision, Vol. 92, No. 2, 132-145, April 2011.
    Invited Paper.
    Revised version of Technical Report No. 236, Department of Mathematics, Saarland University, Saarbrücken, Germany, May 2009.
71. B. Burgeth, L. Pizarro, M. Breuß, J. Weickert:
    Adaptive continuous-scale morphology for matrix fields.
    International Journal of Computer Vision, Vol. 92, No. 2, 146-161, April 2011.
    Invited Paper.
    Revised version of Technical Report No. 237, Department of Mathematics, Saarland University, Saarbrücken, Germany, May 2009.
70. H. Zimmer, A. Bruhn, J. Weickert:
    Freehand HDR imaging of moving scenes with simultaneous resolution enhancement.
    Computer Graphics Forum (Proc. Eurographics 2011, Llandudno, April 11-15, 2011), Vol. 30, No. 2, 405-414, April 2011.
    Also available as Technical Report No. 286, Department of Mathematics, Saarland University, Saarbrücken, Germany, December 2010.
69. M. Breuß, H. Zimmer, J. Weickert:
    Can variational models for correspondence problems benefit from upwind discretisations?
    Journal of Mathematical Imaging and Vision, Vol. 39, No. 3, 230-244, March 2011.
    Revised version of Technical Report No. 254, Department of Mathematics, Saarland University, Saarbrücken, Germany, December 2009 (revised June 2010).
68. N. Ludwig, A. Keller, S. Heisel, P. Leidinger, S. Rheinheimer, C. Andres, B. Stephan, W. I. Steudel, E. Donauer, N. Graf, B. Burgeth, J. Weickert, H.-P. Lenhof, E. Meese:
    Novel immunogenic antigens increase classification accuracy in meningioma to 93.84 %.
    International Journal of Cancer, Vol. 128, No. 6, 1493-1501, March 2011.
67. S. Grewenig, S. Zimmer, J. Weickert:
    Rotationally invariant similarity measures for nonlocal image denoising.
    Journal of Visual Communication and Image Representation, Vol. 22, No. 2, 117-130, February 2011.
    Revised version of Technical Report No. 265, Department of Mathematics, Saarland University, Saarbrücken, Germany, July 2010.
66. T. Teuber, G. Steidl, P. Gwosdek, C. Schmaltz, J. Weickert:
    Dithering by differences of convex functions.
    SIAM Journal on Imaging Sciences, Vol. 4, No. 1, 79-108, January 2011.
    Revised version of Technical Report No. Pr-2010-01, Department of Mathematics, University of Mannheim, Germany, April 2010.
65. C. Schmaltz, P. Gwosdek, A. Bruhn, J. Weickert:
    Electrostatic halftoning.
    Computer Graphics Forum, Vol. 29, No. 8, 2313-2327, December 2010.
    Revised version of Technical Report No. 260, Department of Mathematics, Saarland University, Saarbrücken, Germany, February 2010.
    See also: Supplementary Material Webpage.
64. L. Pizarro, P. Mrázek, S. Didas, S. Grewenig, J. Weickert:
    Generalised nonlocal image smoothing.
    International Journal of Computer Vision, Vol. 90, No. 1, 62-87, October 2010.
    Revised version of Technical Report No. 248, Department of Mathematics, Saarland University, Saarbrücken, Germany, September 2009.
63. P. Gwosdek, A. Bruhn, J. Weickert:
    Variational optic flow on the Sony PlayStation 3 – Accurate dense flow fields for real-time applications.
    Journal of Real-Time Image Processing, Vol. 5, No. 3, 163-177, September 2010.
    Revised version of Technical Report No. 233, Department of Mathematics, Saarland University, Saarbrücken, Germany, March 2009.
62. S. Didas, G. Steidl, J. Weickert:
    Integrodifferential equations for multiscale wavelet shrinkage: The discrete case.
    International Journal of Electrical and Computer Engineering Systems, Vol. 1, No. 1, 5-21, May 2010.
    Revised version of Technical Report No. 214, Department of Mathematics, Saarland University, Saarbrücken, Germany, July 2008.
61. T. Brox, M. Rousson, R. Deriche, J. Weickert:
    Colour, texture, and motion in level set based segmentation and tracking.
    Image and Vision Computing, Vol. 28, No. 3, 376-390, March 2010.
    Revised version of Technical Report No. 147, Department of Mathematics, Saarland University, Saarbrücken, Germany, 2005.
60. N. Ludwig, A. Keller, S. Heisel, P. Leidinger, V. Klein, S. Rheinheimer, C. Andres, B. Stephan, W. I. Steudel, N. Graf, B. Burgeth, J. Weickert, H.-P. Lenhof, E. Meese:
    Improving seroreactivity based detection of glioma.
    Neoplasia, Vol. 11, No. 12, 1383-1389, Dec. 2009.
59. Z. Belhachmi, D. Bucur, B. Burgeth, J. Weickert:
    How to choose interpolation data in images.
    SIAM Journal on Applied Mathematics, Vol. 70, No. 1, 333-352, 2009.
    Revised version of Technical Report No. 205, Department of Mathematics, Saarland University, Saarbrücken, Germany, 2008.
58. S. Didas, J. Weickert, B. Burgeth:
    Properties of higher order nonlinear diffusion filtering.
    Journal of Mathematical Imaging and Vision, Vol. 35, 208-226, 2009.
    Revised version of Technical Report No. 215, Department of Mathematics, Saarland University, Saarbrücken, Germany, August 2008.
57. A. Keller, N. Ludwig, S. Heisel, P. Leidinger, C. Andres, W.-I. Steudel, H. Huwer, B. Burgeth, M. Hein, J. Weickert, E. Meese, H.-P. Lenhof:
    Large-scale antibody profiling of human blood sera: The future of molecular diagnosis.
    Informatik Spektrum, Vol. 32, No. 4, 332-338, 2009.
56. I. Galić, J. Weickert, M. Welk, A. Bruhn, A. Belyaev, H.-P. Seidel:
    Image compression with anisotropic diffusion.
    Journal of Mathematical Imaging and Vision, Vol. 31, 255–269, 2008.
    Invited Paper.
55. M. Welk, G. Steidl, J. Weickert:
    Locally analytic schemes: a link between diffusion filtering and wavelet shrinkage.
    Applied and Computational Harmonic Analysis, Vol. 24, 195–224, 2008.
54. B. Burgeth, S. Didas, L. Florack, J. Weickert:
    A generic approach to diffusion filtering of matrix-fields.
    Computing, Vol. 81, No. 2-3, 179-197, Nov. 2007.
    Invited Paper.
53. I. Sommer, O. Müller, F. S. Domingues, O. Sander, J. Weickert, T. Lengauer:
    Moment invariants as shape recognition technique for comparing protein binding sites.
    Bioinformatics, Vol. 23, No. 23, 3139-3146, Oct. 2007.
52. T. Schuster, J. Weickert:
    On the application of projection methods for computing optical flow fields.
    Inverse Problems and Imaging, Vol. 1, No. 4, 673-690, 2007.
    Revised version of Technical Report No. 194, Department of Mathematics, Saarland University, Saarbrücken, Germany, May 2007.
51. B. Rosenhahn, T. Brox, J. Weickert:
    Three-dimensional shape knowledge for joint image segmentation and pose tracking.
    International Journal of Computer Vision, Vol. 73, No. 3, 243-262, July 2007.
    Revised version of Technical Report No. 163, Centre for Imaging Technology and Robotics, University of Auckland, New Zealand, 2005.
50. B. Burgeth, A. Bruhn, S. Didas, J. Weickert, M. Welk:
    Morphology for tensor data: Ordering versus PDE-based approach.
    Image and Vision Computing, Vol. 25, No. 4, 496-511, 2007.
    Revised version of Technical Report No. 162, Department of Mathematics, Saarland University, Saarbrücken, Germany, December 2005.
49. M. Welk, J. Weickert, I. Galić:
    Theoretical foundations for spatially discrete 1-D shock filtering.
    Image and Vision Computing, Vol. 25, No. 4, 455–463, 2007.
    Revised version of Technical Report No. 150, Department of Mathematics, Saarland University, Saarbrücken, Germany, 2005.
48. P. Mrázek, J. Weickert:
    From two-dimensional nonlinear diffusion to coupled Haar wavelet shrinkage.
    Journal of Visual Communication and Image Representation, Vol. 18, No. 2, 162-175, 2007.
    Revised version of Preprint No. 132, DFG Priority Programme 1114, University of Bremen, Germany, August 2005.
47. S. Didas, J. Weickert:
    Integrodifferential equations for continuous multiscale wavelet shrinkage.
    Inverse Problems and Imaging, Vol. 1, No. 1, 47-62, 2007.
46. S. Didas, J. Weickert:
    B. Burgeth, N. Papenberg, A. Bruhn, M. Welk, J. Weickert:
    Mathematical morphology for tensor data induced by the Loewner ordering in higher dimensions.
    Signal Processing, Vol. 87, No. 2, 277-290, February 2007.
    Revised version of Technical Report No. 161, Department of Mathematics, Saarland University, Saarbrücken, Germany, December 2005.
45. M. Welk, J. Weickert, F. Becker, C. Schnörr, C. Feddern, B. Burgeth:
    Median and related local filters for tensor-valued images.
    Signal Processing, Vol. 87, No. 2, 291-308, February 2007.
    Revised version of Technical Report No. 135, Department of Mathematics, Saarland University, Saarbrücken, Germany, April 2005.
44. Y.-J. Kim, T. Brox, W. Feiden, J. Weickert:
    Fully automated segmentation and morphometrical analysis of muscle fibre images.
    Cytometry: Part A, Vol. 71A, No. 1, 8-15, 2007.
    Revised version of Technical Report No. 177, Department of Mathematics, Saarland University, Saarbrücken, Germany, July 2006.
43. A. Bruhn, J. Weickert, T. Kohlberger, C. Schnörr:
    A multigrid platform for real-time motion computation with discontinuity-preserving variational methods.
    International Journal of Computer Vision, Vol. 70, No. 3, 257-277, December 2006.
    Invited Paper.
    
42. T. Brox, J. Weickert:
    Level set segmentation with multiple regions.
    IEEE Transactions on Image Processing, Vol. 15, No. 10, 3213-3218, October 2006.
    Revised version of Technical Report No. 145, Department of Mathematics, Saarland University, Saarbrücken, Germany, 2005.
41. T. Brox, J. Weickert:
    A TV flow based local scale estimate and its application to texture discrimination.
    Journal of Visual Communication and Image Representation, Vol. 17, No. 5, 1053-1073, October 2006.
    Revised version of Technical Report No. 134, Department of Mathematics, Saarland University, Saarbrücken, Germany, 2005.
40. M. Breuß, J. Weickert:
    A shock-capturing algorithm for the differential equations of dilation and erosion.
    Journal of Mathematical Imaging and Vision, Vol. 25, 187-201, 2006.
    Revised version of Technical Report No. 153, Department of Mathematics, Saarland University, Saarbrücken, Germany, September 2005.
39. C. Feddern, J. Weickert, B. Burgeth, M. Welk:
    Curvature-driven PDE methods for matrix-valued images.
    International Journal of Computer Vision, Vol. 69, No. 1, 91-103, Aug. 2006.
    Revised version of Technical Report No. 104, Department of Mathematics, Saarland University, Saarbrücken, Germany, April 2004.
38. N. Papenberg, A. Bruhn, T. Brox, S. Didas, J. Weickert:
    Highly accurate optic flow computation with theoretically justified warping.
    International Journal of Computer Vision, Vol. 67, No. 2, 141-158, April 2006.
    Revised version of Technical Report No. 124, Department of Mathematics, Saarland University, Saarbrücken, Germany, 2005.
37. T. Brox, J. Weickert, B. Burgeth, P. Mrázek:
    Nonlinear structure tensors.
    Image and Vision Computing, Vol. 24, No. 1, 41-55, Jan. 2006.
    Revised version of Technical Report No. 113, Department of Mathematics, Saarland University, Saarbrücken, Germany, 2004.
36. M. Breuß, T. Brox, A. Bürgel, T. Sonar, J. Weickert:
    Numerical aspects of TV flow.
    Numerical Algorithms, Vol. 41, No. 1, 79-101, 2006.
    Revised version of Technical Report No. 148, Department of Mathematics, Saarland University, Saarbrücken, Germany, August 2005.
35. B. Burgeth, J. Weickert:
    An explanation for the logarithmic connection between linear and morphological system theory.
    International Journal of Computer Vision, Vol. 64, No. 2/3, 157-169, Sept. 2005.
    Revised version of Technical Report No. 95, Department of Mathematics, Saarland University, Saarbrücken, Germany, 2003.
34. P. Mrázek, J. Weickert, G. Steidl:
    Diffusion-inspired shrinkage functions and stability results for wavelet denoising.
    International Journal of Computer Vision, Vol. 64, No. 2/3, 171-186, Sept. 2005.
33. T. Kohlberger, C. Schnörr, A. Bruhn, J. Weickert:
    Domain decomposition for variational optical flow computation.
    IEEE Transactions on Image Processing, Vol. 14, No. 8, 1125-1137, August 2005.
32. A. Bruhn, J. Weickert, C. Feddern, T. Kohlberger, C. Schnörr:
    Variational optic flow computation in real-time.
    IEEE Transactions on Image Processing, Vol. 14, No. 5, 608-615, May 2005.
    Revised version of Technical Report No. 89, Department of Mathematics, Saarland University, Saarbrücken, Germany, June 2003.
31. A. Bruhn, J. Weickert, C. Schnörr:
    Lucas/Kanade meets Horn/Schunck: Combining local and global optic flow methods.
    International Journal of Computer Vision, Vol. 61, No. 3, 211-231, February/March 2005.
    Revised version of Technical Report No. 82, Department of Mathematics, Saarland University, Saarbrücken, Germany, April 2003.
30. G. Steidl, J. Weickert, T. Brox, P. Mrázek, M. Welk:
    On the equivalence of soft wavelet shrinkage, total variation diffusion, total variation regularization, and SIDEs.
    SIAM Journal on Numerical Analysis, Vol. 42, No. 2, 686-713, 2004.
    Shortened version of Technical Report No. 94, Dept. of Mathematics, Saarland University, Saarbrücken, Germany, August 2003.
29. A. Bruhn, T. Jacob, M. Fischer, T. Kohlberger, J. Weickert, U. Brüning, C. Schnörr:
    High performance cluster computing with 3-D nonlinear diffusion filters.
    Real-Time Imaging, Vol. 10, No. 1, 41-51, 2004.
    Revised version of Technical Report No. 87, Dept. of Mathematics, Saarland University, Saarbrücken, Germany, May 2003.
28. D. Cremers, F. Tischhäuser, J. Weickert, C. Schnörr,
    Diffusion snakes: introducing statistical shape knowledge into the Mumford-Shah functional,
    International Journal of Computer Vision, Vol. 50, No. 3, 295-313, December 2002.
27. E. Meijering, W. Niessen, J. Weickert, M. Viergever:
    Diffusion-enhanced visualization and quantification of vascular anomalies in three-dimensional rotational angiography: results of an in-vitro evaluation.
    Medical Image Analysis, Vol. 6, No. 3, 217-235, September 2002.
    Preprint.
26. W. Hinterberger, O. Scherzer, C. Schnörr, J. Weickert:
    Analysis of optical flow models in the framework of calculus of variations.
    Numerical Functional Analysis and Optimization, Vol. 23, No. 1/2, 69-89, May 2002
    Revised version of Technical Report No. 8/2001, Computer Science Series, University of Mannheim, Germany, April 2001.
25. L. Alvarez, R. Deriche, J. Sánchez, J. Weickert:
    Dense disparity map estimation respecting image derivatives: a PDE and scale-space based approach.
    Journal of Visual Communication and Image Representation, Vol. 13, No. 1/2, 3-21, March/June 2002.
    Revised version of Technical Report No. 3874, ROBOTVIS, INRIA Sophia-Antipolis, France, January 2000.
    [Demo]
24. J. Weickert, H. Scharr:
    A scheme for coherence-enhancing diffusion filtering with optimized rotation invariance.
    Journal of Visual Communication and Image Representation, Vol. 13, No. 1/2, 103-118, March/June 2002.
    Revised and shortened version of Technical Report No. 4/2000, Computer Science Series, University of Mannheim, Germany, February 2000.
23. J. Weickert, C. Schnörr:
    A theoretical framework for convex regularizers in PDE-based computation of image motion.
    International Journal of Computer Vision, Vol. 45, No. 3, pp. 245-264, December 2001.
    Revised version of Technical Report No. 13/2000, Computer Science Series, University of Mannheim, Germany, June 2000.
22. J. Weickert:
    Applications of nonlinear diffusion in image processing and computer vision.
    Acta Mathematica Universitatis Comenianae, Vol. 70, No. 1, 33-50, 2001.
    Invited Paper.
21. J. Weickert:
    Efficient image segmentation using partial differential equations and morphology.
    Pattern Recognition, Vol. 34, No. 9, 1813-1824, September 2001.
    Also available as Technical Report No. 3/2000, Computer Science Series, University of Mannheim, Germany, February 2000.
    [Abstract]
20. J. Weickert, C. Schnörr:
    Variational optic flow computation with a spatio-temporal smoothness constraint.
    Journal of Mathematical Imaging and Vision, Vol. 14, No. 3, 245-255, May 2001.
    Revised version of Technical Report No. 15/2000, Computer Science Series, University of Mannheim, Germany, July 2000.
19. J. Weickert, J. Heers, C. Schnörr, K. J. Zuiderveld, O. Scherzer, H. S. Stiehl:
    Fast parallel algorithms for a broad class of nonlinear variational diffusion approaches,
    Real-Time Imaging, Vol. 7, 31-45, No. 1, February 2001.
    Revised version of Technical Report No. 5/1999, Computer Science Series, University of Mannheim, Germany, 1999.
18. L. Alvarez, J. Weickert, J. Sánchez:
    Reliable estimation of dense optical flow fields with large displacements.
    International Journal of Computer Vision, Vol. 39, No. 1, 41-56, August 2000.
    Revised version of Technical Report No. 2, Instituto Universitario de Ciencias y Tecnologias Ciberneticas, Universidad de Las Palmas de Gran Canaria, Spain, November 1999.
    [Demo]
17. J. Weickert, C. Schnörr:
    PDE-based preprocessing of medical images.
    Künstliche Intelligenz, No. 3, 5-10, 2000.
    Revised version of Technical Report No. 8/2000, Computer Science Series, University of Mannheim, Germany, February 2000.
16. E. Radmoser, O. Scherzer, J. Weickert:
    Scale-space properties of nonstationary iterative regularization methods.
    Journal of Visual Communication and Image Representation, Vol. 11, No. 2, 96-114, 2000.
    Invited Paper.
    Also available as Technical Report No. 8/1999, Computer Science Series, University of Mannheim, Germany, October 1999.
15. O. Scherzer, J. Weickert:
    Relations between regularization and diffusion filtering.
    Journal of Mathematical Imaging and Vision, Vol. 12, No. 1, 43-63, February 2000.
    Revised version of Technical Report DIKU-98/23, Dept. of Computer Science, University of Copenhagen, Denmark, 1998.
    [Abstract]
14. J. Sporring, M. Nielsen, O. F. Olsen, J. Weickert:
    Smoothing images creates corners.
    Image and Vision Computing, Vol. 18, No. 3, 261-266, February 2000.
    Revised version of Technical Report DIKU-98/1, Dept. of Computer Science, University of Copenhagen, Denmark, 1998.
    [Abstract]
13. J. Weickert, S. Ishikawa, A. Imiya:
    Linear scale-space has first been proposed in Japan.
    Journal of Mathematical Imaging and Vision, Vol. 10, No. 3, 237-252, May 1999.
    Revised version of Technical Report DIKU-97/18, Dept. of Computer Science, University of Copenhagen, Denmark, 1997.
    [Abstract]
12. J. Weickert:
    Coherence-enhancing diffusion filtering.
    International Journal of Computer Vision, Vol. 31, No. 2, 111-127, April 1999.
    [Abstract]
11. W. J. Niessen, K. L. Vincken, J. Weickert, B. M. ter Haar Romeny, M. A. Viergever:
    Multiscale segmentation of three-dimensional MR brain images.
    International Journal of Computer Vision, Vol. 31, No. 2, 185-202, April 1999.
    [Abstract]
10. J. Sporring, J. Weickert:
    Information measures in scale-spaces.
    IEEE Transactions on Information Theory, Vol. 45, No. 3, 1051-1058, April 1999.
    [Abstract]
9. J. Weickert:
   Coherence-enhancing diffusion of colour images.
   Image and Vision Computing, Vol. 17, No. 3-4, 201-212, March 1999.
   [Abstract]
8. J. Weickert:
   A real-time algorithm for assessing inhomogeneities in fabrics.
   Real-Time Imaging, Vol. 5, No. 1, 15-22, February 1999.
   [Abstract]
7. B. M. ter Haar Romeny, K. J. Zuiderveld, P. F. G. M. van Waes, T. van Walsum, R. van der Weijden, J. Weickert, R. Stokking, O. Wink, S. Kalitzin, T. Maintz, F. Zonneveld, M. A. Viergever:
   Advances in three-dimensional diagnostic radiology.
   Journal of Anatomy, Vol. 193, 363-371, October 1998.
   [Abstract]
6. J. Weickert, B. M. ter Haar Romeny, M. A. Viergever:
   Efficient and reliable schemes for nonlinear diffusion filtering.
   IEEE Transactions on Image Processing, Vol. 7, No. 3, 398-410, March 1998.
   [Abstract]
5. E. P. Stuijfzand, M. D. Abràmoff, K. J. Zuiderveld, L. M. P. Ramos, J. Weickert, M. P. Mourits, F. W. Zonneveld, W. P. T. H. Mali:
   Fast kinematic MR imaging of the eye and orbit.
   RSNA Electronic Journal, Vol. 1, 1997.
   [Abstract]
4. W. J. Niessen, K. L. Vincken, J. Weickert, M. A. Viergever:
   Nonlinear multiscale representations for image segmentation,
   Computer Vision and Image Understanding, Vol. 66, No. 2, 233-245, May 1997.
   [Abstract]
3. J. Weickert:
   Theoretical foundations of anisotropic diffusion in image processing.
   Computing, Suppl. 11, 221-236, 1996.
   [Abstract]
2. J. Weickert:
   Foundations and applications of nonlinear anisotropic diffusion filtering.
   Zeitschrift für Angewandte Mathematik und Mechanik, Vol. 76, Suppl. 1, 283-286, January 1996.
1. J. Weickert:
   A mathematical model for diffusion and exchange phenomena in Ultra napkins.
   Mathematical Methods in the Applied Sciences, Vol. 16, No. 11, 759-777, November 1993.
   Also available as Technical Report No. 72, Laboratory of Technomathematics, University of Kaiserslautern, Germany, June 1992.
   [Abstract]

199. K. Schrader, J. Weickert, M. Krause:
     Anisotropic diffusion stencils: From simple derivations over stability estimates to ResNet implementations.
     To appear in Progress on Industrial Mathematics at ECMI 2023 (Wroclaw, Poland, June 2023), Springer, Cham, 2024.
     Also available as arXiv:2309.05575 [math.NA], revised April 2024.
198. T. Fischer, P. Peter, J. Weickert, E. Ilg:
     Neuroexplicit diffusion models for inpainting of optical flow fields.
     Proc. 41st International Conference on Machine Learning (ICML 2024, Vienna, Austria, July 2024), Proceedings of Machine Learning Research Vol. 235, 13691-13705, 2024
197. F. Jost, V. Chizhov, J. Weickert:
     Optimising different feature types for inpainting-based image representations.
     Proc. 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023, Rhodes, Greece, June 2023), 2023.
     Also available as arXiv:2210.14949 [eess.IV], October 2022.
196. K. Schaefer, J. Weickert:
     Diffusion-shock inpainting.
     In L. Calatroni, M. Donatelli, S. Morigi, M. Prato, M. Santavesaria (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 14009, Springer, Cham, 588-600, 2023.
     Also available as arXiv:2303.09450 [eess.IV], March 2023.
195. K. Schrader, P. Peter, N. Kämper, J. Weickert:
     Efficient neural generation of 4K masks for homogeneous diffusion inpainting.
     In L. Calatroni, M. Donatelli, S. Morigi, M. Prato, M. Santavesaria (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 14009, Springer, Cham, 16-28, 2023.
     Also available as arXiv:2303.10096 [eess.IV], March 2023.
194. K. Schaefer, J. Weickert:
     Stabilised inverse flowline evolution for anisotropic image sharpening.
     Proc. 10th European Workshop on Visual Information Processing (EUVIP 2022, Lisbon, Portugal, Sept. 2022), IEEE, 2022.
     Also available as arXiv:2207.09779 [eess.IV], July 2022.
193. K. Schrader, T. Alt, J. Weickert, M. Ertel:
     CNN-based Euler's elastica inpainting with deep energy and deep image prior.
     Proc. 10th European Workshop on Visual Information Processing (EUVIP 2022, Lisbon, Portugal, Sept. 2022), IEEE, 2022.
     Also available as arXiv:2207.07921 [cs.CV], July 2022.
192. N. Kämper, J. Weickert:
     Domain decomposition algorithms for real-time homogeneous diffusion inpainting in 4K.
     Proc. 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2022, Singapore, May 2022), 1680-1684, 2022.
     Also available as arXiv:2110.03946 [eess.IV], revised February 2022.
191. T. Alt, P. Peter, J. Weickert:
     Learning sparse masks for diffusion-based image inpainting.
     In A. J. Pinho, P. Georgieva, L. F. Teixeira, J. A. Sánchez (Eds.): Pattern Recognition and Image Analysis. Lecture Notes in Computer Science, Vol. 13256, Springer, Cham, 528-539, 2022.
     Also available as arXiv:2110.02636 [eess.IV], revised March 2022.
190. V. Chizhov, J. Weickert:
     Efficient data optimisation for harmonic inpainting with finite elements.
     In N. Tsapatsoulis, A. Panayides, T. Theocharides, A. Lanitis, C.S. Pattichis, M. Vento (Eds.): Computer Analysis of Images and Patterns. Part 2. Lecture Notes in Computer Science, Vol. 13053, Springer, Cham, 432-441, 2021.
     Also available as arXiv:2105.01586 [eess.IV], revised July 2021.
189. S. Andris, J. Weickert, T. Alt, P. Peter:
     JPEG meets PDE-based image compression.
     Proc. 35th Picture Coding Symposium (PCS 2021, Bristol, UK, June 2021), IEEE Press, 2021.
     Also available as arXiv:2102.01138 [eess.IV], revised May 2021.
188. T. Alt, P. Peter, J. Weickert, K. Schrader:
     Translating numerical concepts for PDEs into neural architectures.
     In A. Elmoataz, J. Fadili, Y. Quéau, J. Rabin, L. Simon (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 12679, Springer, Cham, 294-306, 2021.
     Also available as arXiv:2103.15419 [math.NA], March 2021.
187. S. Andris, P. Peter, R. M. K. Mohideen, J. Weickert, S. Hoffmann:
     Inpainting-based video compression in FullHD.
     In A. Elmoataz, J. Fadili, Y. Quéau, J. Rabin, L. Simon (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 12679, Springer, Cham, 425-436, 2021.
     Also available as arXiv:2008.10273 [eess.IV], revised May 2021.
186. K. Bodduna, J. Weickert, M. Cárdenas:
     Multi-frame super-resolution from noisy data.
     In A. Elmoataz, J. Fadili, Y. Quéau, J. Rabin, L. Simon (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 12679, Springer, Cham, 565-577, 2021.
     Also available as arXiv:2103.13778 [eess.IV], March 2021.
185. T. Alt, J. Weickert:
     Learning integrodifferential models for image denoising.
     Proc. 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2021, Toronto, Canada, June 2021), 2045-2049, 2021.
     Also available as arXiv:2010.10888 [eess.IV], October 2020.
184. A. Kardoost, S. Müller, J. Weickert, M. Keuper:
     Object segmentation tracking from generic video cues.
     Proc. 25th International Conference on Pattern Recognition (ICPR 2020, Milan, Italy, January 2021), 623-630, 2021.
     Also available as arXiv:1910.02258 [cs.CV], July 2020.
183. F. Jost, P. Peter, J. Weickert:
     Compressing piecewise smooth images with the Mumford-Shah cartoon model.
     Proc. 28th European Signal Processing Conference (EUSIPCO 2020, Amsterdam, Netherlands, January 2021), 511-515, 2021.
     Also available as arXiv:2003.05206 [eess.IV], March 2020.
182. S. Müller, J. Weickert, N. Graf:
     Wilms' tumor in childhood: Can pattern recognition help for classification?
     In Y. Zheng, B.M. Williams, K. Chen (Eds.): Medical Image Understanding and Analysis. Communications in Computer and Information Science, Vol. 1065, 38-47, Springer, 2020.
181. T. Alt, J. Weickert:
     Learning a generic adaptive wavelet shrinkage function for denoising.
     Proc. 45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020, Barcelona, Spain, May 2020), 2018-2022, 2020.
     Also available as arXiv:1910.09234 [eess.IV], October 2019.
180. F. Jost, P. Peter, J. Weickert:
     Compressing flow fields with edge-aware homogeneous diffusion inpainting.
     Proc. 45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020, Barcelona, Spain, May 2020), 2198-2202, 2020.
     Also available as arXiv:1906.12263 [esss.IV], October 2019.
179. K. Bodduna, J. Weickert:
     Poisson noise removal using multi-frame 3D block matching.
     Proc. 8th European Workshop on Visual Information Processing (EUVIP 2019, Rome, Italy, Oct. 2019), 58-63, 2019.
     Also available as arXiv:1909.08281 [eess.IV], September 2019.
178. K. Bodduna, J. Weickert, A. S. Frangakis:
     Hough based evolutions for enhancing structures in 3D electron microscopy.
     In M. Vento, G. Percannella (Eds.): Computer Analysis of Images and Patterns. Lecture Notes in Computer Science, Vol. 11678, 102-112, Springer, Cham, 2019.
177. L. Bergerhoff, J. Weickert, Y. Dar:
     Algorithms for piecewise constant signal approximations.
     Proc. 27th European Signal Processing Conference (EUSIPCO 2019, A Coruña, Spain, Sept. 2-6, 2019), IEEE, 2019.
     Also available as arXiv:1903.01320v3 [eess.SP], June 2019.
176. K. Bodduna, J. Weickert:
     Enhancing patch-based methods with inter-frame connectivity for denoising multi-frame images.
     Proc. 2019 IEEE International Conference on Image Processing (ICIP 2019, Taipei, Taiwan, Sept. 2019), 2414-2418, 2019.
     Also avalable as arXiv:1906.07109 [eess.IV], June 2019.
175. M. Augustin, J. Weickert, S. Andris:
     Pseudodifferential inpainting: The missing link between PDE- and RBF-based interpolation.
     In J. Lellmann, M. Burger, J. Modersitzki (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 11603, 67-78, Springer, Cham, 2019.
174. P. Peter, J. Contelly, J. Weickert:
     Compressing audio signals with inpainting-based sparsification.
     In J. Lellmann, M. Burger, J. Modersitzki (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 11603, 92-103, Springer, Cham, 2019.
173. M. Welk, J. Weickert:
     PDE evolutions for M-smoothers: From common myths to robust numerics.
     In J. Lellmann, M. Burger, J. Modersitzki (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 11603, 236-248, Springer, Cham, 2019.
172. M. Cardenas, P. Peter, J. Weickert:
     Sparsification scale-spaces.
     In J. Lellmann, M. Burger, J. Modersitzki (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 11603, 303-314, Springer, Cham, 2019.
171. J. A. Tómasson, P. Ochs, J. Weickert:
     AFSI: Adaptive restart for fast semi-iterative schemes for convex optimisation.
     In T. Brox, A. Bruhn, M. Fritz (Eds.): Pattern Recognition. Lecture Notes in Computer Science, Vol. 11269, 669-681, Springer, Cham, 2019.
170. L. Karos, P. Bheed, P. Peter, J. Weickert:
     Optimising data for exemplar-based inpainting.
     In J. Blanc-Talon, D. Helbert, W. Philips, D. Popescu, P. Scheunders (Eds.): Advanced Concepts for Intelligent Vision Systems. Lecture Notes in Computer Science, Vol. 11182, 547-558, Springer, Cham, 2018.
169. L. Bergerhoff, M. Cardenas, J. Weickert, M. Welk:
     Modelling stable backward diffusion and repulsive swarms with convex energies and range constraints.
     In M. Pelillo, E. R. Hancock: Energy Minimization Methods in Computer Vision and Pattern Recognition. Lecture Notes in Computer Science, Vol. 10746, 409-423, Springer, Cham, 2018.
168. M. Welk, J. Weickert:
     An efficient and stable two-pixel scheme for 2D forward-and-backward diffusion.
     In F. Lauze, Y. Dong, A. B. Dahl (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 10302, 94-106, Springer, Cham, 2017.
167. R. D. Adam, P. Peter, J. Weickert:
     Denoising by inpainting.
     In F. Lauze, Y. Dong, A. B. Dahl (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 10302, 121-132, Springer, Cham, 2017.
166. K. Bodduna, J. Weickert:
     Evaluating data terms for variational multi-frame super-resolution.
     In F. Lauze, Y. Dong, A. B. Dahl (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 10302, 590-601, Springer, Cham, 2017.
165. S. Andris, P. Peter, J. Weickert:
     A proof-of-concept framework for PDE-based video compression.
     Proc. 32nd Picture Coding Symposium (Nuremberg, Germany, December 2016).
     PCS 2016 Best Poster Award.

164. M. Schneider, P. Peter, S. Hoffmann, J. Weickert, Enric Meinhardt-Llopis:
     Gradients versus grey values for sparse image reconstruction and inpainting-based compression.
     In J. Blanc-Talon, C. Distante, W. Philips, D. Popescu, P. Scheunders (Eds.): Advanced Concepts for Intelligent Vision Systems. Lecture Notes in Computer Science, Vol. 10016, 1-13, Springer, Cham, 2016.

163. D. Hafner, P. Ochs, J. Weickert, M. Reißel, S. Grewenig:
     FSI schemes: Fast semi-iterative solvers for PDEs and optimisation methods.
     In B. Andres, B. Rosenhahn (Eds.): Pattern Recognition. Lecture Notes in Computer Science, Vol. 9796, 91-102, Springer, Cham, 2016.
     GCPR 2016 Best Paper Award.
162. S. Müller, P. Ochs, J. Weickert, N. Graf:
     Robust interactive multi-label segmentation with an advanced edge detector.
     In B. Andres, B. Rosenhahn (Eds.): Pattern Recognition. Lecture Notes in Computer Science, Vol. 9796, 117-128, Springer, Cham, 2016.
161. S. Müller, J. Weickert, N. Graf:
     Automatic brain tumor segmentation with a fast Mumford-Shah algorithm.
     In M. A. Styner, E. D. Angelini (Eds.): Medical Imaging 2016: Image Processing (San Diego, CA, Febr./March 2016), SPIE Vol. 9784, 97842S, 2016.
160. L. Bergerhoff, J. Weickert:
     Modelling image processing with discrete first-order swarms.
     In N. Pillay, A. P. Engelbrecht, A. Abraham, M. C. du Plessis, V. Snášel, A. K. Muda (Eds.): Advances in Nature and Biologically Inspired Computing. Advances in Intelligent Systems and Computing, Vol. 419, 261-270, Springer, Cham, 2016.
159. P. Peter, S. Hoffmann, F. Nedwed, L. Hoeltgen, J. Weickert:
     From optimised inpainting with linear PDEs towards competitive image compression codecs.
     In T. Bräunl, B. McCane, M. Rivera, X. Yu (Eds.): Image and Video Technology. Lecture Notes in Computer Science, Vol. 9431, 63-74, Springer, Cham, 2016.
158. D. Hafner, C. Schroers, J. Weickert:
     Introducing maximal anisotropy into second order coupling models .
     In J. Gall, P. Gehler, B. Leibe (Eds.): Pattern Recognition. Lecture Notes in Computer Science, Vol. 9358, 79-90, Springer, Berlin, 2015.
157. M. Schmidt, J. Weickert:
     The morphological equivalents of relativistic and alpha-scale-spaces.
     In J.-F. Aujol, M. Nikolova, N. Papadakis (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 9087, 28-39, Springer, Berlin, 2015.
156. G. M. Cárdenas, J. Weickert, S. Schäffer:
     A linear scale-space theory for continuous nonlocal evolutions.
     In J.-F. Aujol, M. Nikolova, N. Papadakis (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 9087, 103-114, Springer, Berlin, 2015.
155. P. Peter, J. Weickert:
     Compressing images with diffusion- and exemplar-based inpainting.
     In J.-F. Aujol, M. Nikolova, N. Papadakis (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 9087, 154-165, Springer, Berlin, 2015.
154. D. Hafner, J. Weickert:
     Variational exposure fusion with optimal local contrast.
     In J.-F. Aujol, M. Nikolova, N. Papadakis (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 9087, 425-436, Springer, Berlin, 2015.
153. C. Schroers, D. Hafner, J. Weickert:
     Multiview depth parameterisation with second order regularisation.
     In J.-F. Aujol, M. Nikolova, N. Papadakis (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 9087, 551-562, Springer, Berlin, 2015.
152. L. Hoeltgen, J. Weickert:
     Why does non-binary mask optimisation work for diffusion-based image compression?.
     In X.-C. Tai, E. Bae, T. F. Chan, M. Lysaker (Eds.): Energy Minimization Methods in Computer Vision and Pattern Recognition. Lecture Notes in Computer Science, Springer, Vol. 8932, 85-98, Berlin, 2015.
151. S. Hoffmann, G. Plonka, J. Weickert:
     Discrete Green's functions for harmonic and biharmonic inpainting with sparse atoms.
     In X.-C. Tai, E. Bae, T. F. Chan, M. Lysaker (Eds.): Energy Minimization Methods in Computer Vision and Pattern Recognition. Lecture Notes in Computer Science, Springer, Vol. 8932, 169-182, Berlin, 2015.
150. P. Peter, J. Weickert, A. Munk, T. Krivobokova, H. Li:
     Justifying tensor-driven diffusion from structure-adaptive statistics of natural images.
     In X.-C. Tai, E. Bae, T. F. Chan, M. Lysaker (Eds.): Energy Minimization Methods in Computer Vision and Pattern Recognition. Lecture Notes in Computer Science, Springer, Vol. 8932, 263-277, Berlin, 2015.
149. P. Peter, J. Weickert:
     Colour image compression with anisotropic diffusion.
     Proc. 21st IEEE International Conference on Image Processing (ICIP 2014, Paris, France, October 2014), 4822-4826, 2014.
148. N. Persch, C. Schroers, S. Setzer, J. Weickert:
     Introducing more physics into variational depth-from-defocus.
     In X. Jiang, J. Hornegger, R. Koch (Eds.): Pattern Recognition. Lecture Notes in Computer Science, Vol. 8753, 15-27, Springer, Berlin, 2014.
     
147. T. Schneevoigt, C. Schroers, J. Weickert:
     A dense pipeline for 3D reconstruction from image sequences.
     In X. Jiang, J. Hornegger, R. Koch (Eds.): Pattern Recognition. Lecture Notes in Computer Science, Vol. 8753, 629-640, Springer, Berlin, 2014.
     
146. O. Demetz, M. Stoll, S. Volz, J. Weickert, A. Bruhn:
     Learning brightness transfer functions for the joint recovery of illumination changes and optical flow.
     In D. Fleet, T. Pajdla, B. Schiele, T. Tuytelaars (Eds.): Computer Vision - ECCV 2014. Lecture Notes in Computer Science, Vol. 8689, 455-471, Springer, Berlin, 2014.
145. D. Hafner, O. Demetz, J. Weickert:
     Simultaneous HDR and optic flow computation.
     Proc. 22nd International Conference on Pattern Recognition (ICPR 2014, Stockholm, Sweden, August 2014), pp. 2065-2070, IEEE Computer Society Press, August 2014.
144. C. Schmaltz, N. Mach, M. Mainberger, J. Weickert:
     Progressive modes in PDE-based image compression.
     Proc. 30th Picture Coding Symposium (PCS 2013, San Jose, CA, Dec. 2013, San Jose, CA), pp. 233-236, IEEE, Piscataway, 2013.

143. S. Gehrig, M. Reznitskii, N. Schneider, U. Franke, J. Weickert:
     Priors for stereo vision under adverse weather conditions.
     Proc. First International Workshop on Computer Vision for Autonomous Driving (CVAD 2013, Sydney, Australia, December 2013), pp. 238-245, IEEE, Piscataway, 2013.

142. O. Demetz, D. Hafner, J. Weickert:
     The complete rank transform: A tool for accurate and morphologically invariant matching of structures.
     In T. Burghardt, D. Damen, W. Mayol-Cuevas, M. Mirmehdi (Eds.): Proc. 24th British Machine Vision Conference. Pages 50.1-50.12, BMVA Press, 2013.
     Awarded the Maria Petrou Prize for Invariance in Computer Vision.
141. A. Hewer, J. Weickert, T. Scheffer, H. Seibert, S. Diebels:
     Lagrangian strain tensor computation with higher order variational models.
     In T. Burghardt, D. Damen, W. Mayol-Cuevas, M. Mirmehdi (Eds.): Proc. 24th British Machine Vision Conference. Pages 129.1-129.10, BMVA Press, 2013.
     
140. J. Weickert, K. Hagenburg, M. Breuß, O. Vogel:
     Linear osmosis models for visual computing.
     In A. Heyden, F. Kahl, C. Olsson, M. Oskarsson, X.-C. Tai (Eds.): Energy Minimization Methods in Computer Vision and Pattern Recognition. Lecture Notes in Computer Science, Vol. 8081, 26-39, Springer, Berlin, 2013.
139. L. Hoeltgen, S. Setzer, J. Weickert:
     An optimal control approach to find sparse data for Laplace interpolation.
     In A. Heyden, F. Kahl, C. Olsson, M. Oskarsson, X.-C. Tai (Eds.): Energy Minimization Methods in Computer Vision and Pattern Recognition. Lecture Notes in Computer Science, Vol. 8081, 151-164, Springer, Berlin, 2013.
138. M. Boshtayeva, D. Hafner, J. Weickert:
     Focus fusion with anisotropic depth map smoothing.
     In A. Bors, E. Hancock, W. Smith, R. Wilson (Eds.): Computer Analysis of Images and Patterns. Lecture Notes in Computer Science, Vol. 8048, 67-74, Springer, Berlin, 2013.
137. D. Hafner, O. Demetz, J. Weickert:
     Why is the census transform good for robust optic flow computation?
     In A. Kuijper, K. Bredies, T. Pock, H. Bischof (Eds.): Scale-Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 7893, 210-221, Springer, Berlin, 2013.
136. S. Hoffmann, M. Mainberger, J. Weickert, M. Puhl:
     Compression of depth maps with segment-based homogeneous diffusion.
     In A. Kuijper, K. Bredies, T. Pock, H. Bischof (Eds.): Scale-Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 7893, 319-330, Springer, Berlin, 2013.
135. O. Vogel, K. Hagenburg, J. Weickert, S. Setzer:
     A fully discrete theory for linear osmosis filtering.
     In A. Kuijper, K. Bredies, T. Pock, H. Bischof (Eds.): Scale-Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 7893, 368-379, Springer, Berlin, 2013.
134. J. Weickert, M. Welk, M. Wickert:
     L2-stable nonstandard finite differences for anisotropic diffusion.
     In A. Kuijper, K. Bredies, T. Pock, H. Bischof (Eds.): Scale-Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 7893, 380-391, Springer, Berlin, 2013.
133. V. Kramarev, O. Demetz, C. Schroers, J. Weickert:
     Cross anisotropic cost volume filtering for segmentation.
     In K. M. Lee, Y. Matsushita, J. M. Rehg, Z. Hu (Eds.): Computer Vision - ACCV 2012. Lecture Notes in Computer Science Vol. 7724, 803-814. Springer, Berlin, 2013.
132. P. Gwosdek, H. Zimmer, S. Grewenig, A. Bruhn, J. Weickert:
     A highly efficient GPU implementation for variational optic flow based on the Euler-Lagrange framework.
     In K. N. Kutulakos (Ed.): Trends and Topics in Computer Vision.
     Lecture Notes in Computer Science, Vol. 6554, 372-383, Springer, Berlin, 2012.
     Revised version of Technical Report No. 267, Department of Mathematics, Saarland University, Saarbrücken, Germany, July 2010.
     See also: Supplementary Material Webpage.
131. C. Schroers, H. Zimmer, L. Valgaerts, A. Bruhn, O. Demetz, J. Weickert:
     Anisotropic range image integration.
     In A. Pinz, T. Pock, H. Bischof, F. Leberl (Eds.): Pattern Recognition. Lecture Notes in Computer Science, Vol. 7476, 73-82, Springer, Berlin, 2012.
     Awarded a DAGM-OAGM 2012 Paper Prize.
130. C. Schmaltz, J. Weickert:
     Video compression with 3-D pose tracking, PDE-based image coding, and electrostatic halftoning.
     In A. Pinz, T. Pock, H. Bischof, F. Leberl (Eds.): Pattern Recognition. Lecture Notes in Computer Science, Vol. 7476, 438-447, Springer, Berlin, 2012.
129. L. Lau Rakêt, L. Roholm, A. Bruhn, J. Weickert:
     Motion compensated frame interpolation with a symmetrical optical flow constraint.
     In G. Bebis, R. Boyle, B. Parvin, D. Koracin, C. Fowlkes, S. Wang, M.-H. Choi, S. Mantler, J. Schulze, D. Acevedo, K. Mueller, M. Papka (Eds.): Advances in Visual Computing, Part I. Lecture Notes in Computer Science, Vol. 7431, 447-457, Springer, Berlin, 2012.

128. M. Mainberger, C. Schmaltz, M. Berg, J. Weickert, M. Backes:
     Diffusion-based image compression in steganography.
     In G. Bebis, R. Boyle, B. Parvin, D. Koracin, C. Fowlkes, S. Wang, M.-H. Choi, S. Mantler, J. Schulze, D. Acevedo, K. Mueller, M. Papka (Eds.): Advances in Visual Computing, Part II. Lecture Notes in Computer Science, Vol. 7432, 219-228, Springer, Berlin, 2012.

127. M. Mainberger, S. Hoffmann, J. Weickert, C. H. Tang, D. Johannsen, F. Neumann, B. Doerr:
     Optimising spatial and tonal data for homogeneous diffusion inpainting.
     In A. M. Bruckstein, B. ter Haar Romeny, A. M. Bronstein, M. M. Bronstein (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 6667, 26-37, Springer, Berlin, 2012.
126. P. Gwosdek, S. Grewenig, A. Bruhn, J. Weickert:
     Theoretical foundations of Gaussian convolution by extended box filtering.
     In A. M. Bruckstein, B. ter Haar Romeny, A. M. Bronstein, M. M. Bronstein (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 6667, 447-458, Springer, Berlin, 2012.
125. K. Hagenburg, M. Breuß, J. Weickert, O. Vogel:
     Novel schemes for hyperbolic PDEs using osmosis filters from visual computing.
     In A. M. Bruckstein, B. ter Haar Romeny, A. M. Bronstein, M. M. Bronstein (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 6667, 532-543, Springer, Berlin, 2012.
124. A. Luxenburger, H. Zimmer, P. Gwosdek, J. Weickert:
     Fast PDE-based image analysis in your pocket.
     In A. M. Bruckstein, B. ter Haar Romeny, A. M. Bronstein, M. M. Bronstein (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 6667, 544-555, Springer, Berlin, 2012.
123. O. Demetz, J. Weickert, A. Bruhn, H. Zimmer:
     Optic flow scale space.
     In A. M. Bruckstein, B. ter Haar Romeny, A. M. Bronstein, M. M. Bronstein (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 6667, 713-724, Springer, Berlin, 2012.
122. A. Elhayek, M. Welk, J. Weickert:
     Simultaneous interpolation and deconvolution model for the 3-D reconstruction of cell images.
     In M. Felsberg, R. Mester (Eds.): Pattern Recognition. Lecture Notes in Computer Science, Vol. 6835, 316-325, Springer, Berlin, 2011.
121. S. Grewenig, J. Weickert, A. Bruhn:
     From box filtering to fast explicit diffusion.
     In M. Goesele, S. Roth, A. Kuijper, B. Schiele, K. Schindler (Eds.): Pattern Recognition.
     Lecture Notes in Computer Science, Vol. 6376, 533-542, Springer, Berlin, 2010.
     Awarded the 2010 DAGM Main Prize (Best Paper Award).
120. L. Valgaerts, A. Bruhn, H. Zimmer, J. Weickert, C. Stoll, C. Theobalt:
     Joint estimation of motion, structure and geometry from stereo sequences.
     In K. Daniilidis, P. Maragos, N. Paragios (Eds.): Computer Vision - ECCV 2010. Lecture Notes in Computer Science, Vol. 6314, 568-581, Springer, Berlin, 2010.
     See also: Supplementary Material Webpage.
119. E. Bae, J. Weickert:
     Partial differential equations for interpolation and compression of surfaces.
     In M. Daehlen, M. Floater, T. Lyche, J.-L. Merrien, K. Mørken, L. L. Schumaker (Eds.): Mathematical Methods for Curves and Surfaces. Lecture Notes in Computer Science, Vol. 5862, 1-14, Springer, Berlin, 2010.
118. K. Hagenburg, M. Breuß, O. Vogel, J. Weickert, M. Welk:
     A lattice Boltzmann model for rotationally invariant dithering.
     In G. Bebis, R. Boyle, B. Parvin, D. Koracin, Y. Kuno, J. Wang, R. Pajarola, P. Lindstrom, A. Hinkenjann, M. L. Encarnaçao, C. T. Silva, D. Coming (Eds.): Advances in Visual Computing. Lecture Notes in Computer Science, Vol. 5876, 949-959, Springer, Berlin, 2009.
117. C. Schmaltz, B. Rosenhahn, T. Brox, J. Weickert:
     Localised mixture models in region-based tracking.
     In J. Denzler, G. Notni, H. Süße (Eds.): Pattern Recognition. Lecture Notes in Computer Science, Vol. 5748, 21-30, Springer, Berlin, 2009.
116. C. Schmaltz, J. Weickert, and A. Bruhn:
     Beating the quality of JPEG 2000 with anisotropic diffusion.
     In J. Denzler, G. Notni, H. Süße (Eds.): Pattern Recognition. Lecture Notes in Computer Science, Vol. 5748, 452-461, Springer, Berlin, 2009.
115. O. Vogel, L. Valgaerts, M. Breuß, and J. Weickert:
     Making shape from shading work for real-world images.
     In J. Denzler, G. Notni, H. Süße (Eds.): Pattern Recognition. Lecture Notes in Computer Science, Vol. 5748, 191-200, Springer, Berlin, 2009
114. M. Mainberger and J. Weickert:
     Edge-based image compression with homogeneous diffusion.
     In X. Jiang, N. Petkov (Eds.): Computer Analysis of Images and Patterns. Lecture Notes in Computer Science, Vol. 5702, Springer, Berlin, 476-483, 2009.

113. H. Zimmer, A. Bruhn, J. Weickert, L. Valgaerts, A. Salgado, B. Rosenhahn, H. P. Seidel:
     Complementary optic flow.
     In D. Cremers, Y. Boykov, A. Blake, F. R. Schmidt (Eds.): Energy Minimization Methods in Computer Vision and Pattern Recognition. Lecture Notes in Computer Science, Vol. 5681, 207-220, Springer, Berlin, 2009.
112. L. Pizarro, B. Burgeth, M. Breuß, J. Weickert:
     A directional Rouy-Tourin scheme for adaptive matrix-valued morphology.
     In M. H. F. Wilkinson, J. B. T. M. Roerdink (Eds.): Mathematical Morphology and Its Application to Signal and Image Processing. Lecture Notes in Computer Science, Vol. 5720, 250-260, Springer, Berlin, 2009.
111. M. Ghodstinat, A. Bruhn, J. Weickert:
     Deinterlacing with motion-compensated anisotropic diffusion.
     In D. Cremers, B. Rosenhahn, A. Yuille, F. Schmidt (Eds.): Statistical and Geometrical Approaches to Visual Motion Analysis. Lecture Notes in Computer Science, Vol. 5604, pp. 91-106, Springer, Berlin, 2009.
110. M. Breuß, J. Weickert:
     Highly accurate PDE-based morphology for general structuring elements.
     In X.-C. Tai, K. Mørken, M. Lysaker, K.-A. Lie (Eds.): Scale-Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 5567, 758-769, Springer, Berlin, 2009.
109. B. Burgeth, M. Breuß, L. Pizarro, J. Weickert:
     PDE-driven adaptive morphology for matrix fields.
     In X.-C. Tai, K. Mørken, M. Lysaker, K.-A. Lie (Eds.): Scale-Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 5567, 247-258, Springer, Berlin, 2009.
108. M. Welk, G. Gilboa, J. Weickert:
     Theoretical foundations for discrete forward-and-backward diffusion filtering.
     In X.-C. Tai, K. Mørken, M. Lysaker, K.-A. Lie (Eds.): Scale-Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 5567, 527-538, Springer, Berlin, 2009.
107. O. Vogel, T. Leichtweis, M. Breuß, J. Weickert:
     Fast shape from shading for Phong-type surfaces.
     In X.-C. Tai, K. Mørken, M. Lysaker, K.-A. Lie (Eds.): Scale-Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 5567, 733-744, Springer, Berlin, 2009.
106. H. Zimmer, M. Breuß, J. Weickert, H.-P. Seidel:
     Hyperbolic numerics for variational approaches to correspondence problems.
     In X.-C. Tai, K. Mørken, M. Lysaker, K.-A. Lie (Eds.): Scale-Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 5567, 636-647, Springer, Berlin, 2009.
105. M. Breuß, O. Vogel, J. Weickert:
     Efficient numerical techniques for perspective shape from shading.
     In A. Handlovicova, P. Frolkovic, K. Mikula, D. Sevcovic (Eds.): Algoritmy 2009 (Podbanske, Slovakia, March 2009). Pages 11-20, Slovak University of Technology, Bratislava, 2009.
104. D. Han, B. Rosenhahn, J. Weickert:
     Combined registration methods for pose estimation.
     In G. Bebis, R. Boyle, B. Parvin, D. Koracin, P. Remagnino, F. Porikli, J. Peters, J. Klosowski, L. Arns, Y. K. Chun, T.-M. Rhyne, L. Monroe (Eds.): Advances in Visual Computing. Lecture Notes in Computer Science, Vol. 5358, 913-924, Springer, Berlin, 2008.
103. L. Pizarro, B. Burgeth, S. Didas, J. Weickert:
     A generic neighbourhood filtering framework for matrix fields.
     In D. Forsyth, P. Torr, A. Zisserman (Eds.): Computer Vision - ECCV 2008. Lecture Notes in Computer Science, Vol. 5304, 521-532, Springer, Berlin, 2008.
102. P. Gwosdek, A. Bruhn, J. Weickert:
     High performance parallel optical flow algorithms on the Sony Playstation 3.
     In O. Deussen, D. Keim, D. Saupe (Eds.): Vision, Modeling, and Visualization 2008. Pages 253-262, AKA, Heidelberg, October 2008.
101. H. Zimmer, A. Bruhn, L. Valgaerts, M. Breuß, J. Weickert, B. Rosenhahn, H.-P. Seidel:
     PDE-based anisotropic disparity-driven stereo vision.
     In O. Deussen, D. Keim, D. Saupe (Eds.): Vision, Modeling, and Visualization 2008. Pages 263-272, AKA, Heidelberg, October 2008.
100. L. Wietzke, G. Sommer, C. Schmaltz, J. Weickert:
     Analysis of the curvature tensor from the viewpoint of signal processing.
     In T. E. Simos, G. Maroulis, G. Psihoyios, Ch. Tsitouras (Eds.): Selected Papers from ICNAAM-2007 and ICCMSE-2007. AIP Conference Proceedings, Vol. 1046, 150-153, American Institute of Physics, Melville, 2008.
99. S. Zimmer, S. Didas, J. Weickert:
    A rotationally invariant block matching strategy improving image denoising with non-local means.
    Proc. 2008 International Workshop on Local and Non-Local Approximation in Image Processing (August 2008, Lausanne, Switzerland).
98. C. Schmaltz, B. Rosenhahn, T. Brox, J. Weickert, L. Wietzke, G. Sommer:
    Dealing with self-occlusion in region based motion capture by means of internal regions.
    In F. J. Perales, R.B. Fischer (Eds.): Articulated Motion and Deformable Objects. Lecture Notes in Computer Science, Vol. 5098, 102-111, Springer, Berlin, 2008.
97. B. Rosenhahn, C. Schmaltz, T. Brox, J. Weickert, H.-P. Seidel:
    Staying well grounded in markerless motion capture.
    In G. Rigoll (Ed.): Pattern Recognition. Lecture Notes in Computer Science, Vol. 5096, 385-395, Springer, Berlin, 2008.
96. L. Valgaerts, A. Bruhn, J. Weickert:
    A variational approach for the joint recovery of the optical flow and the fundamental matrix.
    In G. Rigoll (Ed.): Pattern Recognition. Lecture Notes in Computer Science, Vol. 5096, 314-324, Springer, Berlin, 2008.
95. O. Vogel, M. Breuß, J. Weickert:
    Perspective shape from shading with non-Lambertian reflectance.
    In G. Rigoll (Ed.): Pattern Recognition. Lecture Notes in Computer Science, Vol. 5096, 517-526, Springer, Berlin, 2008.
94. M. Mainberger, A. Bruhn, J. Weickert:
    Is dense optical flow useful to compute the fundamental matrix?
    Updated version with errata. In A. Campilho, M. Kamel (Eds.): Image Analysis and Recognition. Lecture Notes in Computer Science, Vol. 5112, 630-639, Springer, Berlin, 2008.
    
93. S. Barbieri, M. Welk, J. Weickert:
    Variational registration of tensor-valued images.
    Proc. CVPR Workshop »Tensors in Image Processing and Computer Vision«, Anchorage, Alaska, USA, 23 June 2008, pages 1-6.
    
92. B. Rosenhahn, C. Schmaltz, T. Brox, J. Weickert, D. Cremers, H.-P. Seidel:
    Markerless motion capture of man-machine interaction.
    Proc. 2008 IEEE Conference of Computer Vision and Pattern Recognition (Anchorage, AK, June 2008). IEEE Computer Society Press, 2008.
    
91. S. Didas, G. Steidl, J. Weickert:
    Discrete multiscale wavelet shrinkage and integrodifferential equations.
    In P. Schelkens, T. Ebrahimi, G. Christobal, F. Truchetet (Eds.): Optical and Digital Image Processing - Photonics Europe. SPIE Vol. 7000, 2008.
90. L. Wietzke, G. Sommer, C. Schmaltz, J. Weickert:
    Differential geometry of monogenic signal representations.
    In B. McDonald, U. Franke, R. Klette, G. Sommer (Eds.): Robot Vision. Lecture Notes in Computer Science, Vol. 4931, 454-465, 2008.
89. O. Vogel, M. Breuß, J. Weickert:
    A direct numerical approach to perspective shape-from-shading
    In H. Lensch, B. Rosenhahn, H.-P. Seidel, P. Slusallek, J. Weickert (Eds.): Vision, Modeling, and Visualization 2007. Pages 91-100, MPI-I Saarbrücken and AKA, Berlin, 2007.
88. Y. Mileva, A. Bruhn, J. Weickert:
    Illumination-robust variational optical flow with photometric invariants.
    In F. A. Hamprecht, C. Schnörr, B. Jähne (Eds.): Pattern Recognition. Lecture Notes in Computer Science, Vol. 4713, 152-162, Springer, Berlin, 2007.
87. C. Schmaltz, B. Rosenhahn, T. Brox, J. Weickert, D. Cremers, L. Wietzke, G. Sommer:
    Occlusion modeling by tracking multiple objects.
    In F. A. Hamprecht, C. Schnörr, B. Jähne (Eds.): Pattern Recognition. Lecture Notes in Computer Science, Vol. 4713, 173-183, Springer, Berlin, 2007.
86. L. Pizarro, S. Didas, F. Bauer, J. Weickert:
    Evaluating a general class of filters for image denoising
    In B. K. Ersbøll, K. S. Pedersen (Eds.): Image Analysis. Lecture Notes in Computer Science, Vol. 4522, 601-610, Springer, Berlin, 2007.
85. N. Slesareva, T. Bühler, K. Hagenburg, J. Weickert, A. Bruhn, Z. Karni, H.-P. Seidel:
    Robust variational reconstruction from multiple views.
    In B. K. Ersbøll, K. S. Pedersen (Eds.): Image Analysis. Lecture Notes in Computer Science, Vol. 4522, 173-182, Springer, Berlin, 2007.
84. M. Breuß, B. Burgeth, J. Weickert:
    Anisotropic continuous-scale morphology.
    In J. Martí, J. M. Benedí, A. M. Mendonca, J. Serrat (Eds.): Pattern Recognition and Image Analysis. Part I. Lecture Notes in Computer Science, Vol. 4487, 515-522, Springer, Berlin, 2007.
83. C. Schmaltz, B. Rosenhahn, T. Brox, D. Cremers, J. Weickert, L. Wietzke, G. Sommer:
    Region-based pose tracking.
    In J. Martí, J. M. Benedí, A. M. Mendonca, J. Serrat (Eds.): Pattern Recognition and Image Analysis. Part II. Lecture Notes in Computer Science, Vol. 4478, 56-63, Springer, Berlin, 2007.
82. B. Burgeth, S. Didas, L. Florack, J. Weickert:
    A generic approach for singular PDEs for the processing of matrix fields.
    In F. Sgallari, F. Murli, N. Paragios (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 4485, 556-567, Springer, Berlin, 2007.
81. O. Demetz, J. Weickert, A. Bruhn, M. Welk:
    Beauty with variational methods: An optic flow approach to hairstyle simulation.
    In F. Sgallari, F. Murli, N. Paragios (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 4485, 825-836, Springer, Berlin, 2007.
80. S. Didas, J. Weickert:
    Combining curvature motion and edge-preserving denoising.
    In F. Sgallari, F. Murli, N. Paragios (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 4485, 568-579, Springer, Berlin, 2007.
79. E. Hodneland, X.-C. Tai, J. Weickert, N. Bukoreshtliev, H.-H. Gerdes:
    Level set methods for watershed image segmentation.
    In F. Sgallari, F. Murli, N. Paragios (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 4485, 178-190, Springer, Berlin, 2007.
78. O. Vogel, A. Bruhn, J. Weickert, S. Didas:
    Direct shape-from-shading with adaptive higher order regularisation.
    In F. Sgallari, F. Murli, N. Paragios (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 4485, 871-882, Springer, Berlin, 2007.
77. S. Didas, P. Mrázek, J. Weickert:
    Energy-based image simplification with nonlocal data and smoothness terms.
    In A. Iske, J. Levesley (Eds.): Algorithms for Approximation. Pages 50-60, Springer, Heidelberg, 2006.
76. T. Schultz, B. Burgeth, J. Weickert:
    Flexible segmentation and smoothing of DT-MRI fields through a customizable structure tensor.
    In G. Bebis, R. Boyle, B. Parvin, D. Koracin, P. Remagnino, A. V. Nefian, M. Gopi, V. Pascucci, J. Zara, J. Molineros, H. Theisel, T. Malzbender (Eds.): Advances in Visual Computing. Lecture Notes in Computer Science, Vol. 4291, 454-464, Springer, Berlin, 2006.
    Awarded the ISVC 2006 Best Paper Award.
75. S. Didas, J. Weickert:
    From adaptive averaging to accelerated nonlinear diffusion filtering.
    In K. Franke et al. (Eds.): Pattern Recognition. Lecture Notes in Computer Science, Vol. 4174, 101-110, Springer, Berlin, 2006.
74. T. Brox, A. Bruhn, J. Weickert:
    Variational motion segmentation with level sets.
    In H. Bischof, A. Leonardis, A. Pinz (Eds.): Computer Vision - ECCV 2006. Lecture Notes in Computer Science, Vol. 3951, 471–483. Springer, Berlin, 2006.
73. M. Welk, J. Weickert, G. Steidl:
    From tensor-driven diffusion to anisotropic wavelet shrinkage.
    In H. Bischof, A. Leonardis, A. Pinz (Eds.): Computer Vision - ECCV 2006. Lecture Notes in Computer Science, Vol. 3951, 391–403. Springer, Berlin, 2006.
72. T. Brox, Y.-J. Kim, J. Weickert, W. Feiden:
    Fully-automated analysis of muscle fiber images with combined region and edge based active contours.
    In H. Handels, J. Ehrhardt, A. Horsch, H. P. Meinzer, T. Tolxdorff (Eds.): Bildverarbeitung in der Medizin. Pages 86-90, Springer, Berlin, 2006.
71. I. Galić, J. Weickert, M. Welk, A. Bruhn, A. Belyaev, H.-P. Seidel:
    Towards PDE-based image compression.
    In N. Paragios, O. Faugeras, T. Chan, C. Schnörr (Eds.): Variational, Geometric, and Level Set Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 3752, 37-48, Springer, Berlin, 2005.
70. A. Bruhn, J. Weickert:
    Towards ultimate motion estimation: Combining highest accuracy with real-time performance.
    Proc. Tenth IEEE International Conference on Computer Vision, Vol. 1, 749-755, IEEE Computer Society Press, 2005.
69. T. Brox, B. Rosenhahn, J. Weickert:
    Three-dimensional shape knowledge for joint image segmentation and pose estimation.
    In W. Kropatsch, R. Sablatnig, A. Hanbury (Eds.): Pattern Recognition. Lecture Notes in Computer Science, Vol. 3663, 109-116, Springer, Berlin, 2005.
68. S. Didas, J. Weickert, B. Burgeth:
    Stability and local feature enhancement of higher order nonlinear diffusion filtering.
    In W. Kropatsch, R. Sablatnig, A. Hanbury (Eds.): Pattern Recognition. Lecture Notes in Computer Science, Vol. 3663, 451-458, Springer, Berlin, 2005.
67. N. Slesareva, A. Bruhn, J. Weickert:
    Optic flow goes stereo: A variational method for estimating discontinuity-preserving dense disparity maps.
    In W. Kropatsch, R. Sablatnig, A. Hanbury (Eds.): Pattern Recognition. Lecture Notes in Computer Science, Vol. 3663, 33-40, Springer, Berlin, 2005.
    Awarded a DAGM 2005 Paper Prize.
66. M. Welk, D. Theis, J. Weickert:
    Variational deblurring of images with uncertain and spatially variant blurs.
    In W. Kropatsch, R. Sablatnig, A. Hanbury (Eds.): Pattern Recognition. Lecture Notes in Computer Science, Vol. 3663, 485-492, Springer, Berlin, 2005.
65. B. Burgeth, S. Didas, J. Weickert:
    The Bessel scale-space.
    In O. F. Olsen, L. Florack, A. Kuijper (Eds.): Deep Structure, Singularities, and Computer Vision. Lecture Notes in Computer Science, Vol. 3753, 84-95, Springer, Berlin, 2005.
64. B. Burgeth, N. Papenberg, A. Bruhn, M. Welk, C. Feddern, J. Weickert:
    Morphology for higher-dimensional tensor data via Loewner ordering .
    In C. Ronse, L. Najman, E. Decencière (Eds.): Mathematical Morphology: 40 Years On. Computational Imaging and Vision, Vol. 30, 407-418, Springer, Dordrecht, 2005.
63. M. Welk, J. Weickert:
    Semidiscrete and discrete well-posedness of shock filtering.
    In C. Ronse, L. Najman, E. Decencière (Eds.): Mathematical Morphology: 40 Years On. Computational Imaging and Vision, Vol. 30, 311-320, Springer, Dordrecht, 2005.
62. M. Breuss, T. Brox, T. Sonar, J. Weickert:
    Stabilised nonlinear inverse diffusion for approximating hyperbolic PDEs.
    In R. Kimmel, N. Sochen, J. Weickert (Eds.): Scale-Space and PDE Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 3459, 536-547, Springer, Berlin, 2005.
61. A. Bruhn, J. Weickert, T. Kohlberger, C. Schnörr:
    Discontinuity-preserving computation of variational optic flow in real-time.
    In R. Kimmel, N. Sochen, J. Weickert (Eds.): Scale-Space and PDE Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 3459, 279-290, Springer, Berlin, 2005.
60. B. Burgeth, S. Didas, J. Weickert:
    Relativistic scale-spaces.
    In R. Kimmel, N. Sochen, J. Weickert (Eds.): Scale-Space and PDE Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 3459, 1-12, Springer, Berlin, 2005.
59. S. Didas, B. Burgeth, A. Imiya, J. Weickert:
    Regularity and scale-space properties of fractional high order linear filtering.
    In R. Kimmel, N. Sochen, J. Weickert (Eds.): Scale-Space and PDE Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 3459, 13-25, Springer, Berlin, 2005.
58. M. Welk, F. Becker, C. Schnörr, J. Weickert:
    Matrix-valued filters as convex programs.
    In R. Kimmel, N. Sochen, J. Weickert (Eds.): Scale-Space and PDE Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 3459, 204-216, Springer, Berlin, 2005.
57. M. Welk, A. Bergmeister, J. Weickert:
    Denoising of audio data by nonlinear diffusion.
    In R. Kimmel, N. Sochen, J. Weickert (Eds.): Scale-Space and PDE Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 3459, 598-609, Springer, Berlin, 2005.
56. M. Welk, D. Theis, T. Brox, J. Weickert:
    PDE-based deconvolution with forward-backward diffusivities and diffusion tensors.
    In R. Kimmel, N. Sochen, J. Weickert (Eds.): Scale-Space and PDE Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 3459, 585-597, Springer, Berlin, 2005.
55. M. Welk, J. Weickert, G. Steidl:
    A four-pixel scheme for singular differential equations.
    In R. Kimmel, N. Sochen, J. Weickert (Eds.): Scale-Space and PDE Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 3459, 610-621, Springer, Berlin, 2005.
54. T. Brox, J. Weickert:
    Level set based image segmentation with multiple regions.
    In C. E. Rasmussen, H. H. Bülthoff, M. A. Giese, B. Schölkopf (Eds.): Pattern Recognition. Lecture Notes in Computer Science, Vol. 3175, 415-423, Springer, Berlin, 2004.
53. T. Brox, A. Bruhn, N. Papenberg, J. Weickert:
    High accuracy optical flow estimation based on a theory for warping.
    In T. Pajdla, J. Matas (Eds.): Computer Vision - ECCV 2004. Lecture Notes in Computer Science, Vol. 3024, 25-36, Springer, Berlin, 2004.
    2004 Longuet-Higgins Prize by the ECCV (Best Paper Award).
    2014 Jan Koenderink Award for Fundamental Contributions to Computer Vision.
52. T. Brox, J. Weickert:
    A TV flow based local scale measure for texture discrimination.
    In T. Pajdla, J. Matas (Eds.): Computer Vision - ECCV 2004. Lecture Notes in Computer Science, Vol. 3022, 578-590, Springer, Berlin,, 2004.
51. B. Burgeth, M. Welk, C. Feddern, J. Weickert:
    Morphological operations on matrix-valued images.
    In T. Pajdla, J. Matas (Eds.): Computer Vision - ECCV 2004. Lecture Notes in Computer Science, Vol. 3024, 155-167, Springer, Berlin, 2004.
50. T. Kohlberger, C. Schnörr, A. Bruhn, J. Weickert:
    Parallel variational motion estimation by domain decomposition and cluster computing..
    In T. Pajdla, J. Matas (Eds.): Computer Vision - ECCV 2004. Lecture Notes in Computer Science, Vol. 3024, 205-216, Springer, Berlin, 2004.
49. N. Papenberg, A. Bruhn, T. Brox, J. Weickert:
    Numerical justification for multiresolution optical flow computation.
    In L. Alvarez (Ed.): IWCVIA '03: International Workshop on Computer Vision and Image Analysis. Vol. 0026 of Cuadernos del Instituto Universitario de Ciencias y Tecnologias Ciberneticas, pages 7-12, University of Las Palmas de Gran Canaria, Spain, Feb. 2004.
48. J. Weickert, A. Bruhn, N. Papenberg, T. Brox:
    Variational optic flow computation: From continuous models to algorithms.
    In L. Alvarez (Ed.): IWCVIA '03: International Workshop on Computer Vision and Image Analysis. Vol. 0026 of Cuardernos del Instituto Universitario de Ciencias y Technologias Ciberneticas, pages 1-6, University of Las Palmas de Gran Canaria, Spain, Feb. 2004.
47. C. Feddern, J. Weickert, B. Burgeth:
    Level-set methods for tensor-valued images.
    In O. Faugeras, N. Paragios (Eds.): Proc. Second IEEE Workshop on Variational, Geometric and Level Set Methods in Computer Vision. Nice, France, pages 65-72. INRIA, Oct. 2003.
46. D. Slogsnat, M. Fischer, A. Bruhn, J. Weickert, U. Brüning:
    Low level parallelization of nonlinear diffusion filtering algorithms for cluster computing environments.
    In H. Kosch, L. Böszörményi, H. Hellwagner (Eds.): Euro-Par 2003. Parallel Processing. Lecture Notes in Computer Science, Vol. 2790, 481-490, Springer, Berlin, 2003.
45. T. Kohlberger, C. Schnörr, A. Bruhn, J. Weickert:
    Domain decomposition for parallel variational optic flow computation.
    In B. Michaelis, G. Krell (Eds.): Pattern Recognition. Lecture Notes in Computer Science, Vol. 2781, 196-202, Springer, Berlin, 2003.
44. P. Mrázek, J. Weickert:
    Rotationally invariant wavelet shrinkage.
    In B. Michaelis, G. Krell (Eds.): Pattern Recognition. Lecture Notes in Computer Science, Vol. 2781, 156-163, Springer, Berlin, 2003.
43. J. Weickert:
    Coherence-enhancing shock filters.
    In B. Michaelis, G. Krell (Eds.): Pattern Recognition. Lecture Notes in Computer Science, Vol. 2781, 1-8, Springer, Berlin, 2003.
42. M. Welk, C. Feddern, B. Burgeth, J. Weickert:
    Median filtering of tensor-valued images.
    In B. Michaelis, G. Krell (Eds.): Pattern Recognition. Lecture Notes in Computer Science, Vol. 2781, 17-24, Springer, Berlin, 2003.
    Awarded a DAGM 2003 Paper Prize.
41. T. Brox, M. Rousson, R. Deriche, J. Weickert:
    Unsupervised segmentation incorporating colour, texture, and motion.
    In N. Petkov, M. A. Westenberg (Eds.): Computer Analysis of Images and Patterns. Lecture Notes in Computer Science, Vol. 2756, 353-360, Springer, Berlin, 2003.
40. A. Bruhn, J. Weickert, C. Feddern, T. Kohlberger and C. Schnörr:
    Real-time optic flow computation with variational methods.
    In N. Petkov, M. A. Westenberg (Eds.): Computer Analysis of Images and Patterns. Lecture Notes in Computer Science, Vol. 2756, 222-229, Springer, Berlin, 2003.
39. T. Brox, M. Welk, G. Steidl, J. Weickert:
    Equivalence results for TV diffusion and TV regularisation.
    In L. D. Griffin, M. Lillholm (Eds.): Scale Space Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 2695, 86-100, Springer, Berlin, 2003.
38. B. Burgeth, J. Weickert:
    An explanation for the logarithmic connection between linear and morphological systems.
    In L. D. Griffin, M. Lillholm (Eds.): Scale Space Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 2695, 325-339, Springer, Berlin, 2003.
37. P. Mrázek, J. Weickert, G. Steidl:
    Correspondences between wavelet shrinkage and nonlinear diffusion.
    In L. D. Griffin, M. Lillholm (Eds.): Scale-Space Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 2695, 101-116. Springer, 2003.
36. P. Mrázek, J. Weickert, G. Steidl, M. Welk:
    On iterations and scales of nonlinear filters.
    In O. Drbohlav (Ed.): Computer Vision Winter Workshop 2003. Valtice, Czech Republic, pages 61-66, Czech Pattern Recognition Society, January 2003.
35. J. Weickert, T. Brox:
    Diffusion and regularization of vector- and matrix-valued images.
    In M. Z. Nashed, O. Scherzer (Eds.): Inverse Problems, Image Analysis, and Medical Imaging. Contemporary Mathematics, Vol. 313, 251-268, AMS, Providence, 2002.
34. T. Brox, J. Weickert:
    Nonlinear matrix diffusion for optic flow estimation.
    In L. Van Gool (Ed.): Pattern Recognition. Lecture Notes in Computer Science, Vol. 2449, 446-453, Springer, Berlin, 2002.
33. A. Bruhn, T. Jacob, M. Fischer, T. Kohlberger, J. Weickert, U. Brüning C. Schnörr:
    Designing 3-D nonlinear diffusion filters for high performance cluster computing.
    In L. Van Gool (Ed.): Pattern Recognition. Lecture Notes in Computer Science, Vol. 2449, 290-297, Springer, Berlin, 2002.
32. A. Bruhn, J. Weickert, C. Schnörr:
    Combining the advantages of local and global optic flow methods.,
    In L. Van Gool (Ed.): Pattern Recognition. Lecture Notes in Computer Science, Vol. 2449, 454-462, Springer, Berlin, 2002.
    Awarded a DAGM 2002 Paper Prize.
31. G. Kühne, J. Weickert, M. Beier, W. Effelsberg:
    Fast implicit active contour models.
    In L. Van Gool (Ed.): Pattern Recognition. Lecture Notes in Computer Science, Vol. 2449, 133-140, Springer, Berlin, 2002.
30. G. Steidl, J. Weickert:
    Relations between soft wavelet shrinkage and total variation denoising,
    In L. Van Gool (Ed.): Pattern Recognition. Lecture Notes in Computer Science, Vol. 2449, 198-205, Springer, Berlin, 2002.
29. E. Meijering, W. Niessen, J. Weickert, M. Viergever:
    Evaluation of diffusion techniques for improved vessel visualization and quantification in three-dimensional rotational angiography.
    In W. J. Niessen and M. A. Viergever (Eds.): Medical Image Computing and Computer-Assisted Intervention - MICCAI 2001. Lecture Notes in Computer Science, Vol. 2208, 177-185, Springer, Berlin, 2001.
28. G. Kühne, J. Weickert, O. Schuster, S. Richter:
    A tensor-driven active contour model for moving object segmentation.
    Proc. 2001 IEEE International Conference on Image Processing (ICIP-01, Thessaloniki, Oct. 2001). Vol. 2, 73-76, 2001.
27. D. Cremers, C. Schnörr, J. Weickert:
    Diffusion-snakes: Combining statistical shape knowledge and image information in a variational framework.
    Proc. First IEEE Workshop on Variational and Level Set Methods in Computer Vision (VLSM '01, Vancouver, July 13, 2001), pages 137-144, IEEE Computer Society Press, 2001.
    Awarded a VLSM 2001 Paper Prize.
26. L. Alvarez, R. Deriche, J. Sánchez, J. Weickert:
    Dense disparity map estimation respecting image derivatives: a PDE and scale-space based approach.
    Proc. IAPR Workshop on Machine Vision (Tokyo, Nov. 28-30, 2000), 423-427, 2000.
    Most Influential Paper over the Decade Award.
25. D. Cremers, C. Schnörr, J. Weickert, C. Schellewald:
    Learning of translation invariant shape knowledge for steering diffusion snakes.
    In G. Baratoff, H. Neumann (Eds.): Dynamische Perzeption. Proceedings in Artificial Intelligence, Vol. 9, 117-122, Akademische Verlagsgesellschaft Aka, Berlin, 2000.
24. H. Scharr, J. Weickert,
    An anisotropic diffusion algorithm with optimized rotation invariance.
    In G. Sommer, N. Krüger, C. Perwass (Eds.): Mustererkennung 2000, Pages 460-467, Springer, Berlin, 2000.
23. C. Schnörr, J. Weickert:
    Variational image motion computation: theoretical framework, problems and perspectives.
    In G. Sommer, N. Krüger, C. Perwass (Eds.): Mustererkennung 2000. Pages 476-487, Springer, Berlin, 2000.
    Invited Paper.
22. D. Cremers, C. Schnörr, J. Weickert, C. Schellewald:
    Diffusion-snakes using statistical shape knowledge.
    In G. Sommer, Y. Y. Zeevi (Eds.): Algebraic Frames for the Perception-Action Cycle. Lecture Notes in Computer Science, Vol. 1888, 164-174, Springer, Berlin, 2000.
    [Abstract]
21. E. Radmoser, O. Scherzer, J. Weickert:
    Scale-space properties of regularization methods.
    In M. Nielsen, P. Johansen, O. F. Olsen, J. Weickert (Eds.): Scale-Space Theories in Computer Vision. Lecture Notes in Computer Science, Vol. 1682, 211-222, Springer, Berlin, 1999.
20. L. Alvarez, J. Weickert, J. Sánchez:
    A scale-space approach to nonlocal optical flow calculations,
    In M. Nielsen, P. Johansen, O. F. Olsen, J. Weickert (Eds.): Scale-Space Theories in Computer Vision. Lecture Notes in Computer Science, Vol. 1682, 235-246, Springer, Berlin, 1999.
19. J. Weickert, C. Schnörr:
    Räumlich-zeitliche Berechnung des optischen Flusses mit nichtlinearen flußabhängigen Glattheitstermen.
    In W. Förstner, J. M. Buhmann, A. Faber, P. Faber (Eds.): Mustererkennung 1999. Pages 317-324, Springer, Berlin, 1999.
    DAGM 1999 Paper Prize.
18. J. Weickert:
    Fast segmentation methods based on partial differential equations and the watershed transformation:
    In P. Levi, R.-J. Ahlers, F. May, M. Schanz (Eds.): Mustererkennung 1998. Pages 93-100, Springer, Berlin, 1998.
    [Abstract]
17. J. Weickert:
    On discontinuity-preserving optic flow:
    In S. Orphanoudakis, P. Trahanias, J. Crowley, N. Katevas (Eds.): Proc. Computer Vision and Mobile Robotics Workshop (CVMR '98, Santorini, Sept. 17-18, 1998), pages 115-122, 1998.
16. J. Sporring, M. Nielsen, J. Weickert, O. F. Olsen:
    A note on differential corner measures.
    Proc. 14th International Conference on Pattern Recognition (ICPR 14, Brisbane, Aug. 17-20, 1998). Vol. 1, 652-654, IEEE Computer Society Press, Los Alamitos, 1998.
    [Abstract]
15. W. J. Niessen, K. L. Vincken, J. Weickert, M. A. Viergever:
    Three-dimensional MR brain segmentation.
    Proc. Sixth Int. Conf. on Computer Vision (ICCV '98, Bombay, Jan. 4-7, 1998), pages 53-58, 1998.
14. J. Weickert, B. Benhamouda:
    A semidiscrete nonlinear scale-space theory and its relation to the Perona-Malik paradox.
    In F. Solina, W. G. Kropatsch, R. Klette, R. Bajcsy (Eds.): Advances in Computer Vision. Pages 1-10, Springer, Wien, 1997.
    [Abstract]
13. J. Weickert, K. J. Zuiderveld, B. M. ter Haar Romeny, W. J. Niessen:
    Parallel implementations of AOS schemes: A fast way of nonlinear diffusion filtering,
    Proc. 1997 IEEE International Conference on Image Processing (ICIP-97, Santa Barbara, Oct. 26-29, 1997), Vol. 3, 396-399, 1997.
    [Abstract]
12. J. Weickert:
    A review of nonlinear diffusion filtering.
    In B. ter Haar Romeny, L. Florack, J. Koenderink, M. Viergever (Eds.): Scale-Space Theory in Computer Vision. Lecture Notes in Computer Science, Vol. 1252, 3-28, Springer, Berlin, 1997.
    Invited Paper.
    [Abstract]
11. J. Sporring, J. Weickert:
    On generalized entropies and scale-space.
    In B. ter Haar Romeny, L. Florack, J. Koenderink, M. Viergever (Eds.): Scale-Space Theory in Computer Vision. Lecture Notes in Computer Science, Vol. 1252, 53-64, Springer, Berlin, 1997.
    [Abstract]
10. J. Weickert:
    Recursive separable schemes for nonlinear diffusion filters.
    In B. ter Haar Romeny, L. Florack, J. Koenderink, M. Viergever (Eds.): Scale-Space Theory in Computer Vision. Lecture Notes in Computer Science, Vol. 1252, 260-271, Springer, Berlin, 1997.
    [Abstract]
9. J. Weickert:
   Coherence-enhancing diffusion of colour images.
   In A. Sanfeliu, J. J. Villanueva, J. Vitrià (Eds.): Proc. VII National Symposium on Pattern Recognition and Image Analysis (VII NSPRIA, Barcelona, April 21-25, 1997), Vol. 1, 239-244, 1997.
   [Abstract]
8. J. Weickert, B. M. ter Haar Romeny, A. Lopez, W. J. van Enk:
   Orientation analysis by coherence-enhancing diffusion.
   Proc. Symposium on Real World Computing (RWC '97, Tokyo, Jan. 29-31, 1997), pages 96-103, 1997.
   [Abstract]
7. J. Weickert, B. M. ter Haar Romeny, M. A. Viergever:
   Conservative image transformations with restoration and scale-space properties.
   Proc. 1996 IEEE International Conference on Image Processing (ICIP-96, Lausanne, Sept. 16-19, 1996), Vol. 1, 465-468, 1996.
   [Abstract]
6. J. Weickert:
   Nonlinear diffusion scale-spaces: From the continuous to the discrete setting.
   In M.-O. Berger, R. Deriche, I. Herlin, J. Jaffré, J.-M. Morel (Eds.): ICAOS '96: Images, Wavelets and PDEs. Lecture Notes in Control and Information Sciences, Vol. 219, 111-118, Springer, London, 1996.
   [Abstract]
5. J. Weickert:
   A model for the cloudiness of fabrics:
   In H. Neunzert (Ed.): Progress in Industrial Mathematics at ECMI 94. Pages 258-265, Wiley-Teubner, Chichester, 1996.
   Also available as Technical Report No. 131, Laboratory of Technomathematics, University of Kaiserslautern, Germany, February 1995.
   [Abstract]
4. J. Weickert:
   Multiscale texture enhancement.
   In V. Hlavac, R. Sara (Eds.): Computer Analysis of Images and Patterns. Lecture Notes in Computer Science, Vol. 970, 230-237, Springer, Berlin, 1995.
   [Abstract]
3. J. Weickert:
   Ein mathematisches Modell für Ultra-Windeln.
   Proc. Workshop "Mathematische Modelle und Methoden in den Anwendungen" (Mainz, March 19-20, 1992), Vol. ALEF-15, 91-110, 1995.
2. J. Weickert:
   Anisotropic diffusion filters for image processing based quality control.
   In A. Fasano, M. Primicerio (Eds.): Proc. Seventh European Conference on Mathematics in Industry. Pages 355-362, Teubner, Stuttgart, 1994.
   [Abstract]
1. J. Weickert:
   The modelling of napkins.
   In F. Hodnett (Ed.): Proc. Sixth European Conference on Mathematics in Industry. Pages 297-300, Teubner, Stuttgart, 1992.
   [Abstract]

1. C. Hauger, H. Weigand, J. Weickert, A. Bruhn:
   Medizinisch optisches Beobachtungsgerät und Verfahren zum Erstellen einer stereoskopischen Zwischenperspektive in einem derartigen Gerät.
   Patent DE 10 2008 024 732 B4 2010.04.01, April 2010.

3. J. Weickert:
   Partial Differential Equations in Image Processing and Computer Vision.
   Habilitation Thesis, Dept. of Mathematics and Computer Science, University of Mannheim, Germany, January 2001.
2. J. Weickert:
   Anisotropic Diffusion in Image Processing.
   Ph.D. Thesis, Dept. of Mathematics, University of Kaiserslautern, Germany, January 1996.
   An extended and revised version appeared as a book (Teubner, Stuttgart, 1998).
1. J. Weickert,
   Mathematische Modelle eines Diffusions- und Austauschprozesses,
   Diploma Thesis, Dept. of Mathematics, University of Kaiserslautern, Germany, 1990.
   Wacker Memorial Prize of the European Consortium for Mathematics in Industry (ECMI).

8. R. M. K. Mohideen, P. Peter, T. Alt, J. Weickert, A. Scheer:
   Compressing Colour Images with Joint Inpainting and Prediction.
   arXiv:2010.09866 [eess.IV], October 2020.
7. T. Alt, J. Weickert, P. Peter:
   Translating Diffusion, Wavelets, and Regularisation into Residual Networks.
   arXiv:2002.02753 [cs.LG], February 2020.
6. K. Bodduna, J. Weickert:
   Image Denoising with Less Artefacts: Novel Non-linear Filtering on Fast Patch Reorderings.
   arXiv:2002.00638 [eess.IV], February 2020.
5. A. Wewior, J. Weickert:
   Variational Coupling Revisited: Simpler Models, Theoretical Connections, and Novel Applications.
   arXiv:1912.05888 [cs.CV], December 2019.
4. J. Franke, J. Tadjuidje, S. Didas, J. Weickert:
   Some Asymptotics for Local Least-squares Regression with Regularization.
   WIMA Report No. 107, Dept. of Mathematics, University of Kaiserslautern, Germany, 2007.
3. T. Kohlberger, C. Schnörr, A. Bruhn, J. Weickert:
   Domain Decomposition for Nonlinear Problems: A Control-theoretic Approach.
   Technical Report 2005/3, Computer Science Series, University of Mannheim, Germany, April 2005.
2. J. Weickert:
   Scale-space Properties of Nonlinear Diffusion Filtering with a Diffusion Tensor,
   Technical Report No. 110, Laboratory of Technomathematics, University of Kaiserslautern, Germany, October 1994.
   [Abstract]
1. J. Fröhlich, J. Weickert:
   Image Processing Using a Wavelet Algorithm for Nonlinear Diffusion,
   Technical Report No. 104, Laboratory of Technomathematics, University of Kaiserslautern, Germany, March 1994.
   [Abstract]

If you would like a hard copy of any of the previous articles or reports, please e-mail the name of the publication and your physical mail address to: weickert@mia.uni-saarland.de.

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