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Our department has a strong research program in the areas of image processing and image-based pattern recognition. Many results and algorithms we developed are generic in their nature and find their use in numerous application areas. This includes digital photography, surveillance systems, image forensics, biomedicine, remote sensing, astronomy and art conservation.
On this page, we summarize the most important problems we work or worked on. Each area contains a short text explaining in brief results we achieved including links to related journal papers.
Theory of invariants and its application to recognitionDuring last fifteen years we contributed significantly to the theory of moments invariants. This includes derivation of complete systems of invariants with respect to affine transformation, with respect to blurring by kernels with various types of symmetries and also invariants to combined degradations. These results are explained in more detail under the following links.
Our experience with moments and moment invariants gained from years of research in this area resulted in a recent book
covering the current state-of-the-art and presenting the latest developments in this field. Read more here.
Restoration of degraded images
In recent years, we work on algorithms removing a wide variety of degradations that are common in digital imaging.
We consider mainly noise, blurring and insufficient resolution.
The main research topics are
Applications in cultural heritage and art restorationWe have been involved in several cultural heritage applications. Nowadays, art restorers and conservators use various visual sensors for better analysis of old artworks and modern image processing methods can facilitate their work. The following list presents areas where our team was involved
Image ForensicsWe are active in developing mathematical and computational algorithms capable of detecting the traces of tampering in digital images. Below are short descriptions of some of our past and current work in this field.
Other (mostly noteworthy master theses) 
