0533754 20250310153217.320201102235959.9 85090867037 000564245800004 10.1109/TIP.2020.3016490 13 s. P cav_un_epca*02532351057-7149291 (2020)8577-8589Institute of Electrical and Electronics Engineers Blind deconvolution motion deblurring motion estimation shape estimation image matting alternating direction method of multipliers 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. cav_un_auth*0075799 Matas J. CZ 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. pdf 2.4MB http://library.utia.cas.cz/separaty/2020/ZOI/kotera-0533754.pdf https://ieeexplore.ieee.org/document/9171560 GA18-05360S GA ČR cav_un_auth*0361425 We have proposed a method for restoration of fast moving objects from a single image. If an object is photographed at motion in front of a static background, the object will be blurred while the background sharp and partially occluded by the object. The goal is to recover the object appearance from such blurred image. We adopt the image formation model for fast moving objects and consider objects undergoing 2D translation and rotation. For this scenario we formulate the estimation of the object shape, appearance, and motion from a single image and known background as a constrained optimization problem with appropriate regularization terms. Both similarities and differences with blind deconvolution are discussed with the latter caused mainly by the coupling of the object appearance and shape in the acquisition model. Necessary conditions for solution uniqueness are derived and a numerical solution based on the alternating direction method of multipliers is presented. The proposed method is evaluated on a new dataset. WOS JD 20000 20200 20206 2021 3 2 R hod 4 4rh 4 20250310152325.0 4 20250310153217.3 RVO:67985556 http://hdl.handle.net/11104/0312102 cav_un_auth*0376357 ČVUT Fakulta elektrotechnická ČVUT FEL CZ S 3+4 Article Computer Science Artificial Intelligence|Engineering Electrical Electronic C COMPUTERSCIENCE.ARTIFICIALINTELLIGENCE|ENGINEERING.ELECTRICAL&ELECTRONIC 11.491 1.566 6.5 0.07581 3.267 58008 346 1.778 54.89 11.33 16230 Q1 9.662 722 Q1 97.146 95.7 Q1* Q1* 2.31 Q1 96.886 2020 Soubory v repozitáři: kotera-0533754.pdf 85090867037 SCOPUS PUBMED 000564245800004 WOS pdf 2.4MB cav_un_epca*0253235 IEEE Transactions on Image Processing 1057-7149 1941-0042 Roč. 29 č. 1 2020 8577 8589 Institute of Electrical and Electronics Engineers