Fast Bayesian JPEG Decompression and Denoising With Tight Frame Priors

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F17%3A00471741" target="_blank" >RIV/67985556:_____/17:00471741 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/TIP.2016.2627802" target="_blank" >http://dx.doi.org/10.1109/TIP.2016.2627802</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TIP.2016.2627802" target="_blank" >10.1109/TIP.2016.2627802</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Fast Bayesian JPEG Decompression and Denoising With Tight Frame Priors

Popis výsledku v původním jazyce

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 describednby 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 verynefficient. 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 demonstratenthe visual quality of decompression and quantitative experiments compare the algorithm with other state-of-the-art methods.

Název v anglickém jazyce

Fast Bayesian JPEG Decompression and Denoising With Tight Frame Priors

Popis výsledku anglicky

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 describednby 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 verynefficient. 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 demonstratenthe visual quality of decompression and quantitative experiments compare the algorithm with other state-of-the-art methods.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA16-13830S" target="_blank" >GA16-13830S: Perfuzní zobrazování v magnetické rezonanci pomocí komprimovaného snímání</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Transactions on Image Processing

ISSN

1057-7149

e-ISSN

—

Svazek periodika

26

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

490-501

Kód UT WoS článku

000397221700012

EID výsledku v databázi Scopus

2-s2.0-85015224484