Institute of Information Theory and Automation

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Department of Image Processing

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The Department is involved in basic research in image processing and pictorial pattern recognition. Major application areas are biomedicine, remote sensing, astronomy, and art conservation.

Main scientific areas
  • Recognition of distorted images and patterns by invariant descriptors regardless of their actual position in the scene
  • Registration and fusion of several images of the same scene taken at different times, by different sensors and/or from different viewpoints in order to obtain information of higher quality
  • Theory of moment invariants, namely of rotation invariants, affine invariants and invariants to convolution
  • Restoration of degraded images, namely multichannel blind deconvolution, edgepreserving denoising, local contrast enhancement, and color transformations
  • Image forensics - detection of image forgeries
  • Cultural heritage applications - cooperation with art conservators in order to facilitate the conservation and material analysis work
2017-02-08 12:55

Department detail

Ing. Michal Bartoš Ph.D.
RNDr. Zuzana Bílková
RNDr. Jan Blažek Ph.D.
Mgr. Jiří Boldyš Ph.D.
Mgr. Adam Dominec
Ing. Vít Hanousek
Dr. Barmak Honarvar Shakibaei Asli Ph.D.
RNDr. Cyril Höschl Ph.D.
Ing. Jan Kamenický Ph.D.
Ing. Tomáš Kerepecký
Ing. Jitka Kostková
RNDr. Jan Kotera
Mgr. Matěj Lébl
Ing. Babak Mahdian Ph.D.
Jiří Nábělek
Ing. Adam Novozámský Ph.D.
Mgr. Markéta Paroubková
Ing. Stanislav Saic CSc.
Dr. Ing. Jan Schier
RNDr. Michal Šorel Ph.D.
Ing. Lubomír Soukup Ph.D.
Doc. Ing. Filip Šroubek Ph.D. DSc.
Ing. Tomáš Suk Ph.D. DSc.
Jana Švarcová
Ing. Milan Talich Ph.D.
Bc. Oldřich Vlašic
Ing. Hynek Walner
RNDr. Aleš Zita
Doc. RNDr. Barbara Zitová Ph.D.
Duration: 2013 - 2015
In the past decade, we have seen a remarkable growth in ability to capture and distribute digital videos. But, taking into consideration the availability of too many powerful and user-friendly video editors, the question is how to verify the authenticity of these digital videos. The problem becomes crucial, when it comes to videos used in courts as evidences or journalism.
Duration: 2013 - 2016
We addresses one of the key problems of image processing, which is restoration of a latent image from its degraded observations. As the main source of degradation we consider convolution with a unknown blur. The restoration task is a blind deconvolution problem, which is intrinsically ill-posed.
Duration: 2013 - 2015
Automatic processing of microscopic images allows (compared to manual processing by human operator) a better and more efficient acquisition of a large amount of information stored in the captured images. When working with time-lapse images, we need to segment cells from the background, to determine their exact boundaries and monitor their movements.
Duration: 2013 - 2015
Digital image acquisition is often accompanied with its degradation by blur (out-of-focus, motion etc.) and noise. In many cases, the degradation process can be modeled by convolution g=u*h+n where g denotes the acquired image, u the original image, h the convolution mask (blur), and n random noise. The goal of deconvolution is to recover the original image based on the observed image.