Fast Bayesian JPEG Decompression and Denoising With Tight Frame Priors

@article{orel2017FastBJ,
  title={Fast Bayesian JPEG Decompression and Denoising With Tight Frame Priors},
  author={Michal {\vS}orel and Michal Barto{\vs}},
  journal={IEEE Transactions on Image Processing},
  year={2017},
  volume={26},
  pages={490-501}
}
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 $l_{1}$ 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… 

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