Institute of Information Theory and Automation

Publication details

Fast Bayesian JPEG Decompression and Denoising With Tight Frame Priors

Journal Article

Šorel Michal, Bartoš Michal


serial: IEEE Transactions on Image Processing vol.26, 1 (2017), p. 490-501

project(s): GA16-13830S, GA ČR

keywords: image processing, image restoration, JPEG

preview: Download

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

Responsible for information: admin
Last modification: 21.12.2012
Institute of Information Theory and Automation