Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/23451
Title: Sparse nonlocal priors based two-phase approach for mixed noise removal
Authors: Jiang, J
Yang, J
Cui, Y
Wong, WK 
Lai, Z
Keywords: Adaptive regularization
Mixed noise removal
Nonlocal
Sparse representation
Issue Date: 2015
Publisher: Elsevier
Source: Signal processing, 2015, v. 116, 5788, p. 101-11 How to cite?
Journal: Signal processing 
Abstract: 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.
URI: http://hdl.handle.net/10397/23451
ISSN: 0165-1684
EISSN: 1872-7557
DOI: 10.1016/j.sigpro.2015.04.011
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