Mixed noise removal by weighted encoding with sparse nonlocal regularization

Jielin Jiang, Lei Zhang, Jian Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

109 Citations (Scopus)

Abstract

Mixed noise removal from natural images is a challenging task since the noise distribution usually does not have a parametric model and has a heavy tail. One typical kind of mixed noise is additive white Gaussian noise (AWGN) coupled with impulse noise (IN). Many mixed noise removal methods are detection based methods. They first detect the locations of IN pixels and then remove the mixed noise. However, such methods tend to generate many artifacts when the mixed noise is strong. In this paper, we propose a simple yet effective method, namely weighted encoding with sparse nonlocal regularization (WESNR), for mixed noise removal. In WESNR, there is not an explicit step of impulse pixel detection; instead, soft impulse pixel detection via weighted encoding is used to deal with IN and AWGN simultaneously. Meanwhile, the image sparsity prior and nonlocal self-similarity prior are integrated into a regularization term and introduced into the variational encoding framework. Experimental results show that the proposed WESNR method achieves leading mixed noise removal performance in terms of both quantitative measures and visual quality.
Original languageEnglish
Article number6800039
Pages (from-to)2651-2662
Number of pages12
JournalIEEE Transactions on Image Processing
Volume23
Issue number6
DOIs
Publication statusPublished - 1 Jan 2014

Keywords

  • Mixed noise removal
  • Nonlocal
  • Sparse representation
  • Weighted encoding

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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