0474858 20240103214110.120170529235959.9 85018507914 000399396400034 10.1109/TIP.2017.2676981 12 s. P cav_un_epca*02532351057-7149265 (2017)2533-2544Institute of Electrical and Electronics Engineers blind deconvolution variational Bayes automatic relevance determination cav_un_auth*0293863 Kotera Jan Zpracování obrazové informace Department of Image Processing ZOI ZOI UTIA-B Department of Image Processing CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101207 Šmídl Václav Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101209 Šroubek Filip Zpracování obrazové informace Department of Image Processing ZOI ZOI UTIA-B Department of Image Processing Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2017/ZOI/kotera-0474858.pdf cav_un_auth*0292734 GA13-29225S GA ČR cav_un_auth*0314467 GA15-16928S GA ČR Blind deconvolution is a strongly ill-posed problem comprising of simultaneous blur and image estimation. Recent advances in prior modeling and/or inference methodology led to methods that started to perform reasonably well in real cases. However, as we show here, they tend to fail if the convolution model is violated even in a small part of the image. Methods based on variational Bayesian inference play a prominent role. In this paper, we use this inference in combination with the same prior for noise, image, and blur that belongs to the family of independent non-identical Gaussian distributions, known as the automatic relevance determination prior. We identify several important properties of this prior useful in blind deconvolution, namely, enforcing non-negativity of the blur kernel, favoring sharp images over blurred ones, and most importantly, handling non-Gaussian noise, which, as we demonstrate, is common in real scenarios. The presented method handles discrepancies in the convolution model, and thus extends applicability of blind deconvolution to real scenarios, such as photos blurred by camera motion and incorrect focus. JD 20000 20200 20206 2018 3 4 A hod 4ah 20231122142442.8 RVO:67985556 http://hdl.handle.net/11104/0271794 1 Department of Adaptive Systems UTIA-B 10201 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 1 Department of Image Processing UTIA-B 10201 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE S 2 Article Computer Science Artificial Intelligence|Engineering Electrical Electronic 2 Article Computer Science Artificial Intelligence|Engineering Electrical Electronic 2 Article Computer Science Artificial Intelligence|Engineering Electrical Electronic COMPUTERSCIENCE.ARTIFICIALINTELLIGENCE|ENGINEERING.ELECTRICAL&ELECTRONIC 5.853 0.96 7.2 0.05406 1.817 31357 1.374 4.432 448 Q1 91.212 91.5 Q1* Q1* 1.93 Q1 92.045 2017 Soubory v repozitáři: kotera-0474858.pdf 85018507914 SCOPUS 28278468 PUBMED 000399396400034 WOS cav_un_epca*0253235 IEEE Transactions on Image Processing 1057-7149 1941-0042 Roč. 26 č. 5 2017 2533 2544 Institute of Electrical and Electronics Engineers