Image restoration via successive compression

Yehuda Dar, Alfred M. Bruckstein, Michael Elad

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations


In this paper we propose a method for solving various imaging inverse problems via complexity regularization that leverages existing image compression techniques. Lossy compression has already been proposed in the past for Gaussian denoising - the simplest inverse problem. However, extending this approach to more complicated inverse problems (e.g., deblurring, inpainting, etc.) seemed to result in intractable optimization tasks. In this work we address this difficulty by decomposing the complicated optimization problem via the Half Quadratic Splitting approach, resulting in a sequential solution of a simpler l2-regularized inverse problem followed by a rate-distortion optimization, replaced by an efficient compression technique. In addition, we suggest an improved complexity regularizer that quantifies the average block-complexity in the restored signal, which in turn, extends our algorithm to rely on averaging multiple decompressed images obtained from compression of shifted images. We demonstrate the proposed scheme for inpainting of corrupted images, using leading image compression techniques such as JPEG2000 and HEVC.

Original languageEnglish
Title of host publication2016 Picture Coding Symposium, PCS 2016
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Electronic)9781509059669
StatePublished - 19 Apr 2017
Externally publishedYes
Event2016 Picture Coding Symposium, PCS 2016 - Nuremberg, Germany
Duration: 4 Dec 20167 Dec 2016

Publication series

Name2016 Picture Coding Symposium, PCS 2016


Conference2016 Picture Coding Symposium, PCS 2016

ASJC Scopus subject areas

  • Media Technology
  • Signal Processing


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