Patch group based bayesian learning for blind image denoising

Jun Xu, Dongwei Ren, Lei Zhang, David Zhang

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

9 Citations (Scopus)


Most existing image denoising methods assume to know the noise distributions, e.g., Gaussian noise, impulse noise, etc. However, in practice the noise distribution is usually unknown and is more complex, making image denoising still a challenging problem. In this paper, we propose a novel blind image denoising method under the Bayesian learning framework, which automatically performs noise inference and reconstructs the latent clean image. By utilizing the patch group (PG) based image nonlocal self-similarity prior, we model the PG variations as Mixture of Gaussians, whose parameters, including the number of components, are automatically inferred by variational Bayesian method. We then employ nonparametric Bayesian dictionary learning to extract the latent clean structures from the PG variations. The dictionaries and coefficients are automatically inferred by Gibbs sampling. The proposed method is evaluated on images with Gaussian noise, images with mixed Gaussian and impulse noise, and real noisy photographed images, in comparison with state-of-the-art denoising methods. Experimental results show that our proposed method performs consistently well on all types of noisy images in terms of both quantitative measure and visual quality, while those competing methods can only work well on the specific type of noisy images they are designed for and perform poorly on other types of noisy images. The proposed method provides a good solution to blind image denoising.

Original languageEnglish
Title of host publicationComputer Vision - ACCV 2016 Workshops - ACCV 2016 International Workshops, Revised Selected Papers
EditorsJiwen Lu, Kai-Kuang Ma, Chu-Song Chen
Number of pages17
ISBN (Print)9783319544069
Publication statusPublished - 1 Jan 2017
Event13th Asian Conference on Computer Vision, ACCV 2016 - Taipei, Taiwan
Duration: 20 Nov 201624 Nov 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10116 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference13th Asian Conference on Computer Vision, ACCV 2016
City Taipei

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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