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Blind Deconvolution With Model Discrepancies
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SYSNO ASEP 0474858 Druh ASEP J - Článek v odborném periodiku Zařazení RIV J - Článek v odborném periodiku Poddruh J Článek ve WOS Název Blind Deconvolution With Model Discrepancies Tvůrce(i) Kotera, Jan (UTIA-B)
Šmídl, Václav (UTIA-B) RID, ORCID
Šroubek, Filip (UTIA-B) RID, ORCIDCelkový počet autorů 3 Zdroj.dok. IEEE Transactions on Image Processing. - : Institute of Electrical and Electronics Engineers - ISSN 1057-7149
Roč. 26, č. 5 (2017), s. 2533-2544Poč.str. 12 s. Forma vydání Tištěná - P Jazyk dok. eng - angličtina Země vyd. US - Spojené státy americké Klíč. slova blind deconvolution ; variational Bayes ; automatic relevance determination Vědní obor RIV JD - Využití počítačů, robotika a její aplikace Obor OECD Computer hardware and architecture CEP GA13-29225S GA ČR - Grantová agentura ČR GA15-16928S GA ČR - Grantová agentura ČR Institucionální podpora UTIA-B - RVO:67985556 UT WOS 000399396400034 EID SCOPUS 85018507914 DOI 10.1109/TIP.2017.2676981 Anotace 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. Pracoviště Ústav teorie informace a automatizace Kontakt Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Rok sběru 2018
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