Welcome to the homepage of the lecture

Image Processing and Computer Vision

Summer Term 2022

Image Processing and Computer Vision

Three Teaching Awards (2 in Computer Science, 1 in Mathematics)

Prof. Joachim Weickert
Virtual office hour: Tuesday, 14:15 - 15:00.

Coordinator of tutorials: Michael Ertel
Virtual office hour: Wednesday, 9:00 - 10:00.

Summer Term 2022

Lectures (4h) with theoretical and programming assignments (2h);
(9 ETCS points)

Online lectures based on the Zoom platform: (privacy information):
Tuesday, 10:15-12:00
Friday, 10:15-12:00

First lecture: Tuesday, April 12

The Zoom link for forthcoming lectures and the password for downloading the slides and videos have been e-mailed to registered participants on April 15.

Online Tutorials: 2 hours each week; see below.

NewsType of LecturesPrerequisitesTutorialsRegistrationWritten ExamsContentsSelf TestMaterial for the Programming AssignmentsExample Solutions for the AssignmentsLiterature

05. 08. 2022: The results of the first written exam are now online.

05. 08. 2022: Opportunity for exam inspection:
Monday, August 8, Lecture Hall 001, Building E1 3. You can find your time slot for the exam inspection here.

28. 07. 2022: The seating for the first exam is now available.

19. 07. 2022: A list of students admitted to the exams is now available.

15. 05. 2022: Schedule of forthcoming lectures has been updated to include a deep learning lecture (Lecture 29).

15. 04. 2022: Registered students have been added to the mailing list of their tutorial. If you have not received a welcoming email, contact Michael Ertel.

15. 04. 2022: Registered students have been added to the ipcv22 mailing list. If you have not received a welcoming email, contact Michael Ertel.

15. 04. 2022: The registration is now closed.

14. 04. 2022: The registration deadline has been extended by one more day until Friday, April 15, 16:00.

12. 04. 2022: Unfortunately, the wrong Zoom link was announced for today's lecture. This is corrected now, and the video is available. We apologise for any inconveniences. Please do not forget to register by Thursday, 16:00.

07. 04. 2022: The second lecture will take place on Thursday, 14.04.2022, 10:15-12:00 due to a holiday on Friday.

This class gives a broad introduction to the mathematically well-founded and model-based areas of image processing and computer vision. These fields are important in numerous applications including medical image analysis, computer-aided quality control, robotics, computer graphics, multimedia, data science, machine learning, and artificial intelligence. The class is required for starting a bachelor thesis in our group.

It is planned that this class will be continued in the winter term with the class "Differential Equations in Image Processing and Computer Vision" which will bring you closer to our research topics. Both classes are required to pursue a master thesis in our group.

This course is suitable for students of Visual Computing, Mathematics, Computer Science, Mathematics and Computer Science, Data Science and Artificial Intelligence, Bioinformatics, Mechatronics, and Physics. It counts e.g. as a visual computing core area course within the Visual Computing program, and as a core course (Stammvorlesung) within Mathematics or Computer Science.

It is based on undergraduate mathematical knowledge from the first three semesters (such as "Mathematics for Computer Scientists I-III"). For the programming assignments, some elementary knowledge of C is required. The lectures are given in English.

In the online tutorials we discuss the homework assignments (theory and programming). The programming assignments give an intuition about the way how image processing and computer vision algorithms work, while the theoretical assigments provide additional insights, also from a mathematical perspective.

For the homework assignments you can obtain up to 24 points per week. To qualify for both exams you need 50 percent of all possible points. Working in groups of up to three people is permitted and strongly recommended, but all persons must be in the same tutorial group.

If you have questions concerning the tutorials, please do not hesitate to contact Michael Ertel.

Seven online groups are scheduled for Tuesday and Wednesday:

  • Group T1: Tuesday, 12:15-14:00 (tutor understands German).
    Tutor: Elias Endres.
    Office hour: Wednesday, 10:00-11:00.

  • Group T2: Tuesday, 14:15-16:00.
    Tutor: Yassir Janah.
    Office hour: Wednesday, 08:00-09:00.

  • Group T3: Tuesday, 16:15-18:00.
    Tutor: Yassir Janah.
    Office hour: Wednesday, 08:00-09:00.

  • Group W1: Wednesday, 10:15-12:00.
    Tutor: Yassir Janah.
    Office hour: Wednesday, 08:00-09:00.

  • Group W2: Wednesday, 12:15-14:00.
    Tutor: Yassir Janah.
    Office hour: Wednesday, 08:00-09:00.

  • Group W3: Wednesday, 14:15-16:00.
    Tutor: Harishanth Sivakumaran.
    Office hour: Thursday, 09:00-10:00.

  • Group W4: Wednesday, 16:15-18:00.
    Tutor: Harishanth Sivakumaran.
    Office hour: Thursday, 09:00-10:00.

The tutors can be reached via the mail addresses:
ipcv-# -- at -- mia.uni-saarland.de
where # has to be replaced by t1, t2, t3, w1, w2, w3, and w4, respectively.

If you have difficulties with the programming assignments, feel free to consult our

  • Optional Guided Programming (OGP) Helpdesk: Tuesday, 18:15-20:00
    Please send e-mail for online appointments to Michael Ertel.

You could register for this course and the tutorial groups between Tuesday, April 12, 2022, 14:00 and Friday, April 15, 2022, 16:00. You can still check which group you are finally in. We use Zoom as online teaching platform. Please make sure that you have access to it.

Please do not forget to register also in the HISPOS/LSF system (apart from Erasmus students). This system administrates your exam admission and your grades. It will allow registrations in the end of April.

It is planned to have two written exams. Changes due to the development of the COVID-19 pandemic cannot be excluded. We will announce any changes of the exam dates as soon as possible.

The first exam takes place on August 2, 14:00-17:00.
The second exam takes place on October 11, 14:00-17:00.

In order to qualify for the exams you need 50 percent of all possible points from the homework assignments. In case of qualification, you are allowed to take part in both exams. The better grade counts. Each exam counts as an individual attempt.

Both exams will be closed book exams. You will have to follow these rules:

  • You are allowed and obliged to bring three things to your desk only: Your student ID card (Studierendenausweis), a ball-pen that has no function other than writing, and a so-called cheat sheet. This cheat sheet is a A4 page with formulas or important equations from the lecture. Please note that the cheat sheet has to be handwritten by yourself. It will be collected at the end of the exam, and you can get it back at the exam inspection.
  • Everything else has to be deposited at the walls of the exam hall. In particular, electronic devices (including your cell phone), bags, jackets, briefcases, lecture notes, homework and preparatory work solutions, additional handwritten notes, books, dictionaries, and paper are not allowed at your desk.
  • Please keep your student ID card ready for an attendance check during the exam.
  • Do not use pencils or pens that are erasable with a normal rubber.
  • You are not allowed to take anything with you that contains information about the exam.
    A violation of this rule means failing the IPCV course.
  • You must stay until the exam is completely over.
  • You have to wear a medical face mask (satisfying the medical mouth and nose protection, FFP2, or KN95 standards).

If a student is unable to attend the written exams due to reasons beyond his/her control (e.g. because of an illness (medical certificate required immediately), travel restrictions, or another exam at the same day), it is planned to provide alternative options such as an online oral exam.

A list of students that are admitted to the written exams is now available here. If you do not find your matriculation number in the list and you think you should be admitted to the exams, please contact Michael Ertel.

Here is the distribution of places by family name (i.e. surname, last name) for the first exam that takes place on Tuesday, August 2, 2022 from 14:00 to 17:00:

Students A - G: Building E1.3, Lecture Hall 002
Students H - Z: Building E2.2, Günter Hotz Lecture Theatre

The results of the first written exam can be found here, and the corresponding distribution of points and grades here.

Each student who has participated in the first written exam has the opportunity to inspect his/her graded solutions in Lecture hall 001 in Building E1.3 on Monday, August 8, 2022. Everyone is assigned to a time slot for the exam inspection, you can see when you should arrive here.

Course material is available on this webpage in order to support the teaching and the tutorials, not to replace them. Additional organisational information, examples and explanations that may be relevant for your understanding and the exam are provided in the lectures and tutorials. It is solely your responsibility - not ours - to make sure that you receive this infomation. Here is a preliminary list of the planned contents:


Date TopicVideo
12.04. Foundations I: Definitions, Image Types, Discretisation Video
14.04. Foundations II: Degradations in Digital Images
(contains preparatory assignment P1 and homework H1)
19.04. Foundations III: Colour Perception and Colour Spaces Video
22.04. Image Transformations I: Continuous Fourier Transform
(contains preparatory assignment P2 and homework H2)
26.04. Image Transformations II: Sampling Theorem and Discrete Fourier Transform Video
29.04. Image Transformations III: Discrete Cosine Transform and Image Pyramids
(contains preparatory assignment P3 and homework H3)
03.05. Image Transformations IV: Discrete Wavelet Transform
06.05. Image Compression
(contains preparatoy assignment P4 and homework H4)
10.05. Image Interpolation Video


Date TopicVideo
13.05. Point Operations
(contains preparatory assignment P5 and homework H5)
17.05. Linear Filters I: System Theory Video
20.05. Linear Filters II: Derivative Filters
(contains preparatory assignment P6 and homework H6)
24.05. Linear Filters III: Detection of Edges and Corners Video
27.05. Nonlinear Filters I: Morphology and Median Filters
(contains preparatory assignment P7 and homework H7)
31.05. Nonlinear Filters II: Wavelet Shrinkage, Bilateral Filters, NL-Means Video
03.06. Nonlinear Filters III: Nonlinear Diffusion Filtering
(contains preparatory assignment P8 and homework H8)
07.06. Discrete Variational Methods for Image Enhancement Video
10.06. Continuous Variational Methods for Image Enhancement
(contains preparatory assignment P9 and homework H9)
14.06. Fourier-based Image Enhancement and Deconvolution Video
17.06. Texture Analysis
(contains preparatory assignment P10 and homework H10)


Date TopicVideo
21.06. Image Sequence Analysis I: Local Methods Video
24.06. Image Sequence Analysis II: Variational Methods
(contains preparatory assignment P11 and homework H11)
28.06. 3-D Reconstruction I: Camera Geometry Video
01.07. 3-D Reconstruction II: Stereo
(contains preparatory assignment P12 and homework H12)
05.07. 3-D Reconstruction III: Shape-from-Shading Video
08.07. Segmentation
(contains preparatory assignment P13 and homework H13)
12.07. Object Recognition I: Hough Transform and Invariants Video
15.07. Object Recognition II: Eigenspace Methods Video
19.07. Object Recognition III: Neural Networks Video
22.07. Summary, Conclusions, Outlook Video

At the end of the semester there will be a self-test problem sheet containing 6 problems, which are intended to be similar in style and difficulty to a 180-minutes written exam.

The following self-test problem sheet contains 6 problems, which are intended to be similar in style and difficulty to a 180-minutes written exam.

15.07. Self Test Problem Sheet
15.07. Self Test Solution

Here you can download material for the programming assignments.

14.04. Assignment H1: Noise and Quantisation
22.04. Assignment H2: Colour Spaces and Subsampling
29.04. Assignment H3: Fourier Filtering
06.05. Assignment H4: Discrete Cosine Transform
13.05. Assignment H5: Point Transformations
20.05. Assignment H6: Linear Filters
27.05. Assignment H7: Edge and Corner Detection, Morphology
03.06. Assignment H8: Wavelet Shrinkage, NL-Means
10.06. Assignment H9: Whittaker-Tikhonov Regularisation, Unsharp Masking
17.06. Assignment H10: Texture Inpainting, Deconvolution
24.06. Assignment H11: Optic Flow
01.07. Assignment H12: Correlation-Based Stereo Method
08.07. Assignment H13: Mumford–Shah Cartoon Model

Here you will find example solutions for the assignments.

22.04. Assignment P1: Convolution, Box-Muller Algorithm
22.04. Assignment H1: PSNR, Convolution, Noise and Quantisation
29.04. Assignment P2: Colour Spaces, Continuous Fourier Transform
29.04. Assignment H2: Continuous Fourier Transform, Colour Spaces
06.05. Assignment P3: Fourier Spectrum, Discrete Fourier Transform
06.05. Assignment H3: Discrete Fourier Transform,
Image Pyramids , Fourier Filtering
13.05. Assignment P4: Discrete Wavelet Transform
13.05. Assignment H4: Transformation Matrices, DFT and DWT,
Huffman Coding, Discrete Cosine Transform
20.05. Assignment P5: Point Operations
20.05. Assignment H5: Interpolation, B-Spline Interpolation,
Affine Rescaling, Gamma Correction, Histogram Equalisation
27.05. Assignment P6: Linear Filters, Derivative Filters
27.05. Assignment H6: Derivative Filters, Derivative Approximation
Linear Filters
03.06. Assignment P7: Tensor Analysis, Mean and Median Filtering
03.06. Assignment H7: Structure Tensor Analysis,
Morphology, Edge and Corner Detection
10.06. Assignment P8: Average Grey Level Preservation and Extremum Priciple
10.06. Assignment H8: Multiple Choice, Nonlinear Diffusion,
Wavelet Shrinkage, NL-Means
17.06. Assignment P9: Convexity of a Discrete Energy Function,
Euler-Lagrange Equations
17.06. Assignment H9: Variational Methods, Fourier Analysis of Linear
Filters, Whittaker-Tikhonov Regularisation, Unsharp Masking
24.06. Assignment P10: Inverse Filtering, Cooccurrence Matrices
24.06. Assignment H10: Deconvolution, Texture Inpainting
01.07. Assignment P11: Optic Flow Constraint
01.07. Assignment H11: Lucas and Kanade, Variational Optic Flow
08.07. Assignment P12: Transformation Matrices, 3-D Rotation and Cardan Angles
08.07. Assignment H12: Homogeneous Coordinates, Fundamental Matrix
Stereo Reconstruction, Correlation-Based Stereo Method
15.07. Assignment P13: Lambertian Surface, Otsu's Threshold Selection Method
15.07. Assignment H13: 1-D Shape from Shading, Variational Method, Mumford-Shah Cartoon Model

There is no specific text book for this class, but many of our image processing topics are covered in one of the following books:

  • J. Bigun: Vision with Direction. Springer, Berlin, 2010.
  • R. C. Gonzalez, R. E. Woods: Digital Image Processing. Addison-Wesley, International Edition, 2017.
  • K. D. Tönnies: Grundlagen der Bildverarbeitung. Pearson Studium, München, 2005.

Computer vision books include

These and further books can be found in the mathematics and computer science library.
Furthermore, there is an interesting online compendium, where many researchers have written survey articles.
If you are looking for a specific reference, check out the Annotated Computer Vision Bibliography.
Many highly cited articles can be found via the Google Scholar webpage.

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