bibtype	J - Journal Article
ARLID	0471741
utime	20240103213708.8
mtime	20170301235959.9
SCOPUS	85015224484
WOS	000397221700012
DOI	10.1109/TIP.2016.2627802
title (primary) (eng)	Fast Bayesian JPEG Decompression and Denoising With Tight Frame Priors
specification	page_count 12 s. media_type P
serial	ARLID cav_un_epca*0253235 ISSN 1057-7149 title IEEE Transactions on Image Processing volume_id 26 volume 1 (2017) page_num 490-501 publisher name Institute of Electrical and Electronics Engineers
keyword	image processing
keyword	image restoration
keyword	JPEG
author (primary)	ARLID cav_un_auth*0108377 name1 Šorel name2 Michal full_dept (cz) Zpracování obrazové informace full_dept (eng) Department of Image Processing department (cz) ZOI department (eng) ZOI institution UTIA-B full_dept Department of Image Processing fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author	ARLID cav_un_auth*0312355 name1 Bartoš name2 Michal full_dept (cz) Zpracování obrazové informace full_dept Department of Image Processing department (cz) ZOI department ZOI institution UTIA-B full_dept Department of Image Processing country CZ fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source	url http://library.utia.cas.cz/separaty/2017/ZOI/sorel-0471741.pdf
cas_special	project ARLID cav_un_auth*0338628 project_id GA16-13830S agency GA ČR country CZ abstract (eng) 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\nby 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\nefficient. 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\nthe visual quality of decompression and quantitative experiments compare the algorithm with other state-of-the-art methods. RIV JD FORD0 10000 FORD1 10200 FORD2 10201 reportyear 2018 num_of_auth 2 mrcbC52 4 A hod 4ah 20231122142302.3 inst_support RVO:67985556 permalink http://hdl.handle.net/11104/0270739 mrcbC64 1 Department of Image Processing UTIA-B 10200 COMPUTER SCIENCE, THEORY & METHODS confidential S mrcbC86 2 Article Computer Science Artificial Intelligence|Engineering Electrical Electronic mrcbC86 3+4 Article Computer Science Artificial Intelligence|Engineering Electrical Electronic mrcbC86 3+4 Article Computer Science Artificial Intelligence|Engineering Electrical Electronic mrcbT16-e COMPUTERSCIENCEARTIFICIALINTELLIGENCE|ENGINEERINGELECTRICALELECTRONIC mrcbT16-j 1.817 mrcbT16-s 1.374 mrcbT16-B 91.212 mrcbT16-D Q1* mrcbT16-E Q1* arlyear 2017 mrcbTft \nSoubory v repozitáři: sorel-0471741.pdf mrcbU14 85015224484 SCOPUS mrcbU24 PUBMED mrcbU34 000397221700012 WOS mrcbU63 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