0533761 20250121152656.520201102235959.9 85098628719 000646178501006 10.1109/ICIP40778.2020.9191316 5 s. E cav_un_epca*0533760978-1-7281-6396-31522-4880928-932PiscatawayIEEE2020 deblurring deconvolution motion blur atomic norm convolutional sparse coding cav_un_auth*0101209 Šroubek Filip UTIA-B Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0293863 Kotera Jan UTIA-B Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing CZ Ústav teorie informace a automatizace AV ČR, v. v. i. pdf http://library.utia.cas.cz/separaty/2020/ZOI/sroubek-0533761.pdf 0.3MB GA18-05360S GA ČR cav_un_auth*0361425 We have proposed a novel methodology for generating priors that favor motion blur. Priors play an important role of regularizers in image deblurring algorithms. Image priors are frequently studied and many forms were proposed in the literature. Blur priors are considered less important and the most common forms are simple uniform distributions with domain constraints. We propose a more informative blur prior based on the notion of atomic norm which favors blurs composed of line segments and is suitable for motion blur. The prior is formulated as a linear program that can be inserted into any optimization task. Evaluation is conducted on blind deblurring of moving objects. cav_un_auth*0398419 2020 IEEE International Conference on Image Processing (ICIP) 20201025 20201028 Abu Dhabi AE JD 20000 20200 20206 2021 2 4 A sml 4as 20231122145221.5 PR RVO:67985556 http://hdl.handle.net/11104/0312100 S IEEE COPYRIGHT AND CONSENT FORM 20200131 0.366 Q3 2020 Soubory v repozitáři: sroubek-0533761-CopyrightReceipt3.pdf 85098628719 SCOPUS PUBMED 000646178501006 WOS pdf 0.3MB cav_un_epca*0533760 2020 IEEE International Conference on Image Processing (ICIP) IEEE 2020 Piscataway 928 932 978-1-7281-6396-3 1522-4880 2381-8549