Multi-channel Weighted Nuclear Norm Minimization for Real Color Image Denoising

Jun Xu, Lei Zhang, David Zhang, Xiangchu Feng

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

122 Citations (Scopus)

Abstract

Most of the existing denoising algorithms are developed for grayscale images. It is not trivial to extend them for color image denoising since the noise statistics in R, G, and B channels can be very different for real noisy images. In this paper, we propose a multi-channel (MC) optimization model for real color image denoising under the weighted nuclear norm minimization (WNNM) framework. We concatenate the RGB patches to make use of the channel redundancy, and introduce a weight matrix to balance the data fidelity of the three channels in consideration of their different noise statistics. The proposed MC-WNNM model does not have an analytical solution. We reformulate it into a linear equality-constrained problem and solve it via alternating direction method of multipliers. Each alternative updating step has a closed-form solution and the convergence can be guaranteed. Experiments on both synthetic and real noisy image datasets demonstrate the superiority of the proposed MC-WNNM over state-of-the-art denoising methods.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1105-1113
Number of pages9
ISBN (Electronic)9781538610329
DOIs
Publication statusPublished - 22 Dec 2017
Event16th IEEE International Conference on Computer Vision, ICCV 2017 - Venice Convention Center, Venice, Italy
Duration: 22 Oct 201729 Oct 2017

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
Volume2017-October
ISSN (Print)1550-5499

Conference

Conference16th IEEE International Conference on Computer Vision, ICCV 2017
CountryItaly
CityVenice
Period22/10/1729/10/17

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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