Počet záznamů: 1  

Blind Deconvolution With Model Discrepancies

  1. 1.
    0474858 - ÚTIA 2018 RIV US eng J - Článek v odborném periodiku
    Kotera, Jan - Šmídl, Václav - Šroubek, Filip
    Blind Deconvolution With Model Discrepancies.
    IEEE Transactions on Image Processing. Roč. 26, č. 5 (2017), s. 2533-2544. ISSN 1057-7149. E-ISSN 1941-0042
    Grant CEP: GA ČR GA13-29225S; GA ČR GA15-16928S
    Institucionální podpora: RVO:67985556
    Klíčová slova: blind deconvolution * variational Bayes * automatic relevance determination
    Obor OECD: Computer hardware and architecture
    Impakt faktor: 5.072, rok: 2017
    http://library.utia.cas.cz/separaty/2017/ZOI/kotera-0474858.pdf

    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.
    Trvalý link: http://hdl.handle.net/11104/0271794

     
     
Počet záznamů: 1  

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