Fast Bayesian JPEG Decompression and Denoising With Tight Frame Priors

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Fast Bayesian JPEG Decompression and Denoising With Tight Frame Priors
Original language description
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.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
<a href="/en/project/GA16-13830S" target="_blank" >GA16-13830S: Magnetic resonance perfusion imaging using compressed sensing</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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