Nonlocal image denoising via collaborative spatial-domain LMMSE estimation

Bo Wang, Zixiang Xiong, Dongqing Zhang, Heather Yu

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

1 Citation (Scopus)


In recent years, the performance of image denoising has been boosted drastically by nonlocal algorithms and sparse coding techniques. In this paper, we also take a nonlocal approach to image denoising and formulate the problem as one of collaborative LMMSE estimation from grouped image patches. We show that our optimal LMMSE solution amounts to shrinking the singular values of the matrix representation of the grouped image patches. This interpretation of our solution allows us to relate our estimation-theoretic approach to other nonlocal algorithms and sparse coding techniques in the literature. In addition, we develop an iterative algorithm to find the best LMMSE estimate. Experimental results show that our proposed denoising algorithm achieves better PSNR and subjective performance than the state of the art.

Original languageEnglish
Title of host publication2014 IEEE International Conference Image Processing
EditorsPascal Frossard, Marc Antonini
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)9781479957514
Publication statusPublished - 28 Jan 2014
Externally publishedYes
EventIEEE International Conference on Image Processing 2014 - Paris, France
Duration: 27 Oct 201430 Oct 2014
Conference number: 21st (Proceedings)


ConferenceIEEE International Conference on Image Processing 2014
Abbreviated titleICIP 2014
Internet address


  • Image denoising
  • LMMSE estimation
  • nonlocal algorithms
  • sparse coding
  • SVD

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