Patch group based nonlocal self-similarity prior learning for image denoising

Jun Xu, Lei Zhang, Wangmeng Zuo, Dapeng Zhang, Xiangchu Feng

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

232 Citations (Scopus)

Abstract

Patch based image modeling has achieved a great success in low level vision such as image denoising. In particular, the use of image nonlocal self-similarity (NSS) prior, which refers to the fact that a local patch often has many nonlocal similar patches to it across the image, has significantly enhanced the denoising performance. However, in most existing methods only the NSS of input degraded image is exploited, while how to utilize the NSS of clean natural images is still an open problem. In this paper, we propose a patch group (PG) based NSS prior learning scheme to learn explicit NSS models from natural images for high performance denoising. PGs are extracted from training images by putting nonlocal similar patches into groups, and a PG based Gaussian Mixture Model (PG-GMM) learning algorithm is developed to learn the NSS prior. We demonstrate that, owe to the learned PG-GMM, a simple weighted sparse coding model, which has a closed-form solution, can be used to perform image denoising effectively, resulting in high PSNR measure, fast speed, and particularly the best visual quality among all competing methods.
Original languageEnglish
Title of host publication2015 International Conference on Computer Vision, ICCV 2015
PublisherIEEE
Pages244-252
Number of pages9
Volume2015 International Conference on Computer Vision, ICCV 2015
ISBN (Electronic)9781467383912
DOIs
Publication statusPublished - 17 Feb 2015
Event15th IEEE International Conference on Computer Vision, ICCV 2015 - Santiago, Chile
Duration: 11 Dec 201518 Dec 2015

Conference

Conference15th IEEE International Conference on Computer Vision, ICCV 2015
Country/TerritoryChile
CitySantiago
Period11/12/1518/12/15

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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