Supplementary Material Page


Variational Image Fusion with
Optimal Local Contrast


David Hafner and Joachim Weickert

Mathematical Image Analysis Group, Faculty of Mathematics and Computer Science,
Saarland University, Campus E1.7, 66041 Saarbrücken, Germany
{hafner, weickert}@mia.uni-saarland.de


Multispectral imaging Exposure fusion Decolourisation
Exemplary applications of our general variational image fusion technique. Each of the resulting composite images (top) condenses the most important information from the input stack (bottom). (Input images are taken from: left, middle, right)


Abstract
In this paper, we present a general variational method for image fusion. In particular, we combine different images of the same subject to a single composite that offers optimal exposedness, saturation, and local contrast. Previous research approaches this task by first precomputing application specific weights based on the input, and then combining these weights with the images to the final composite later on. In contrast, we design our model assumptions directly on the fusion result. To this end, we formulate the output image as a convex combination of the input and incorporate concepts from perceptually inspired contrast enhancement such as a local and nonlinear response. This output-driven approach it the key to the versatility of our general image fusion model. In this regard, we demonstrate the performance of our fusion scheme with several applications such as exposure fusion, multispectral imaging, and decolourisation. For all application domains, we conduct thorough validations that illustrate the improvements compared to state-of-the-art approaches that are tailored to the individual tasks.


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