0434275 20240111140853.320150106235959.9 84949929119 000370063604133 10.1109/ICIP.2014.7025911 5 s. C cav_un_epca*0434274978-1-4799-5751-44492-4496USAIEEE2014 blind deconvolution variational Bayes automatic relevance determination cav_un_auth*0101209 Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing Šroubek Filip UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101207 Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems Šmídl Václav UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0293863 Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing Kotera Jan UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. pdf http://library.utia.cas.cz/separaty/2014/ZOI/sroubek-0434275.pdf cav_un_auth*0292734 GA13-29225S GA ČR cav_un_auth*0308955 GAUK 938213 Grantová agentura UK CZ Removing blurs from a single degraded image without any knowledge of the blur kernel is an ill-posed blind deconvolution problem. Proper estimators together with correct image priors play a fundamental role in accurate blind deconvolution. We demonstrate a superior performance of the variational Bayesian estimator and discuss suitability of automatic relevance determination distributions as image priors. Restoration of real photos blurred by out-of-focus and motion blur, and comparison with a state-of-the-art method is provided. cav_un_auth*0308954 2014 IEEE International Conference on Image Processing 27.10.2014-30.10.2014 Paris FR JD 2015 PO RVO:67985556 http://hdl.handle.net/11104/0241853 S 2014 84949929119 SCOPUS 000370063604133 WOS pdf cav_un_epca*0434274 2014 IEEE International Conference on Image Processing 978-1-4799-5751-4 4492 4496 USA IEEE 2014