0471741 20240103213708.820170301235959.9 85015224484 000397221700012 10.1109/TIP.2016.2627802 12 s. P cav_un_epca*02532351057-7149261 (2017)490-501Institute of Electrical and Electronics Engineers image processing image restoration JPEG cav_un_auth*0108377 Šorel Michal Zpracování obrazové informace Department of Image Processing ZOI ZOI UTIA-B Department of Image Processing Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0312355 Bartoš Michal Zpracování obrazové informace Department of Image Processing ZOI ZOI UTIA-B Department of Image Processing CZ Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2017/ZOI/sorel-0471741.pdf cav_un_auth*0338628 GA16-13830S GA ČR CZ JPEG decompression can be understood as an image reconstruction problem similar to denoising or deconvolution. Such problems can be solved within the Bayesian maximum a posteriori probability framework by iterative optimization algorithms. Prior knowledge about an image is usually described by the l1 norm of its sparse domain representation. For many problems, if the sparse domain forms a tight frame, optimization by the alternating direction method of multipliers can be very efficient. However, for JPEG, such solution is not straightforward, e.g., due to quantization and subsampling of chrominance channels. Derivation of such solution is the main contribution of this paper. In addition, we show that a minor modification of the proposed algorithm solves simultaneously the problem of image denoising. In the experimental section, we analyze the behavior of the proposed decompression algorithm in a small number of iterations with an interesting conclusion that this mode outperforms full convergence. Example images demonstrate the visual quality of decompression and quantitative experiments compare the algorithm with other state-of-the-art methods. JD 10000 10200 10201 2018 2 4 A hod 4ah 20231122142302.3 RVO:67985556 http://hdl.handle.net/11104/0270739 1 Department of Image Processing UTIA-B 10200 COMPUTER SCIENCE, THEORY & METHODS S 2 Article Computer Science Artificial Intelligence|Engineering Electrical Electronic 3+4 Article Computer Science Artificial Intelligence|Engineering Electrical Electronic 3+4 Article Computer Science Artificial Intelligence|Engineering Electrical Electronic COMPUTERSCIENCE.ARTIFICIALINTELLIGENCE|ENGINEERING.ELECTRICAL&ELECTRONIC 5.853 0.96 7.2 0.05406 1.817 31357 1.374 4.432 448 Q1 91.212 91.5 Q1* Q1* 1.93 Q1 92.045 2017 Soubory v repozitáři: sorel-0471741.pdf 85015224484 SCOPUS PUBMED 000397221700012 WOS cav_un_epca*0253235 IEEE Transactions on Image Processing 1057-7149 1941-0042 Roč. 26 č. 1 2017 490 501 Institute of Electrical and Electronics Engineers