Sparse nonlocal priors based two-phase approach for mixed noise removal

Jielin Jiang, Jian Yang, Yan Cui, Wai Keung Wong, Zhihui Lai

Research output: Journal article publicationJournal articleAcademic researchpeer-review

5 Citations (Scopus)


Abstract Mixed noise removal is a challenging problem due to the complexity of statistical model of image noise. Additive white Gaussian noise (AWGN) combined with impulse noise (IN) is a representative among commonly encountered mixed noise. At present, nonlocal self-similarity (NSS) prior coupled with adaptive regularization have shown great potential in AWGN removal and led to satisfactory denoising performance. However, few studies unify these properties to remove mixture of AWGN and IN. In this paper, we propose a simple yet effective method, namely sparse nonlocal priors based two-phase approach (SNTP), for mixed noise removal. In SNTP, a median-type filter is used to detect outlier pixels which are likely to be corrupted by IN, and the remaining pixels are mainly corrupted by AWGN. We recover the image by encoding free-outlier pixels over a pre-learned dictionary to remove AWGN, and integrate the image sparse nonlocal priors as a regularization term. Meanwhile, adaptive regularization is used to further improve the denoising performance. Experimental results show that the proposed SNTP algorithm outperforms state-of-the-art mixed noise removal methods in terms of both quantitative measures and visual perception quality.
Original languageEnglish
Article number5788
Pages (from-to)101-11
Number of pages89
JournalSignal Processing
Publication statusPublished - 1 Nov 2015


  • Adaptive regularization
  • Mixed noise removal
  • Nonlocal
  • Sparse representation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


Dive into the research topics of 'Sparse nonlocal priors based two-phase approach for mixed noise removal'. Together they form a unique fingerprint.

Cite this