Image Processing

The matlab code for computing optimal optical flow masks for compression is avaible here. Note that the code is hardly documented and provided “as-is”. The code is released under a GPL v3 licence (unless specified differently).

Besides my research in computer vision related tasks such as optical flow, photometric stereo, and shape matching and my focus on PDE-based compression, I have also ventured in other image processing tasks. One of these tasks was mathematical morphology. We developed a partial differential equation based leveling model and used a bi-cone shaped colour space to filter our texture in images. In contrast to classical levelling approaches our method also works for well for colour images.

Over the years I have written quite a lot of Matlab code. The code is available here, here, here, and here and covers mostly image processing and interpolation/approximation topics. All the code is released under a GPL v3 licence (unless specified differently). Note: This code has been rotting on my computers for years now. It’s badly documented, likely to be buggy and probably won’t even run without some fixes. I don’t have access to Matlab any longer and have no plans in using this code any more.

Over the years I have written quite a lot of Mathematica code. The code is available here and here and covers mostly image processing topics. All the code is released under a GPL v3 licence (unless specified differently). Note: This code has been rotting on my computers for years now. It’s badly documented, likely to be buggy and probably won’t even run without some fixes. I have no plans in using this code any more.

We have investigated high performing optimization algorithms and matrix differential calculus technique in the context of Photometric Stereo and presented the results at the BMVC 2016 Source Code A github repository with the code is maintained by Yvain Quéau. A snapshot (master branch, 2020/12/21) of the repository is available as zip Archive on this website.

I’ve developed optimization algorithms for variational optical flow models based on the split Bregman algorithm in my Master thesis. A follow-up investigation on the necessity of certain intermediate filtering steps was published at the EMMCVPR 2011. I’ve also shown that the optical flow can be used in the context of partial differential equation based image compression. The results were presented in two publications at the SSVM 2019 and in the Journal of Mathematical Imaging and Vision.

It is possible to compress/inpaint images from very little data. In order to obtain reconstructions that are comparable to the original image it is necessary to optimize the underlying interpolation data. Several such strategies have been discussed in my PhD Thesis. In a series of subsequent publications (here, here and here) we showed the relationship to other partial differential equations and also stated conditions under which the existence of a unique solution can be asserted.

The aim of this paper is to deal with Poisson noise in images arising in electron microscopy. We consider here especially images …

Morphological levelings represent a useful tool for the decomposition of an image into cartoon and texture components. Moreover, they …