An image denoising algorithm for mixed noise combining nonlocal means filter and sparse representation technique

•A customized blockwise nonlocal means (CBNLM) filter is proposed to generate an initial denoised image.•Different noisy pixels are classified according to three-sigma rule which is related to the noise distribution feature.•A complementary sparse coding technique is used to find the sparse vector f...

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Bibliographic Details
Published inJournal of visual communication and image representation Vol. 41; pp. 74 - 86
Main Authors Zhou, Yingyue, Lin, Maosong, Xu, Su, Zang, Hongbin, He, Hongsen, Li, Qiang, Guo, Jin
Format Journal Article
LanguageEnglish
Published Elsevier Inc 01.11.2016
Subjects
Complementary sparse coding
Image denoising
Mixed noise
Noise classification
Nonlocal means
Sparse representation
Sparse representation
Noise classification
Nonlocal means
Mixed noise
Image denoising
Complementary sparse coding
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Summary:•A customized blockwise nonlocal means (CBNLM) filter is proposed to generate an initial denoised image.•Different noisy pixels are classified according to three-sigma rule which is related to the noise distribution feature.•A complementary sparse coding technique is used to find the sparse vector for each input noisy patch. Nonlocal means (NLM) filtering or sparse representation based denoising method has obtained a remarkable denoising performance. In order to integrate the advantages of two methods into a unified framework, we propose an image denoising algorithm through skillfully combining NLM and sparse representation technique to remove Gaussian noise mixed with random-valued impulse noise. In the non-Gaussian circumstance, we propose a customized blockwise NLM (CBNLM) filter to generate an initial denoised image. Based on it, we classify the different noisy pixels according to the three-sigma rule. Besides, an overcomplete dictionary is trained on the initial denoised image. Then, a complementary sparse coding technique is used to find the sparse vector for each input noisy patch over the overcomplete dictionary. Through solving a more reasonable variational denoising model, we can reconstruct the clean image. Experimental results verify that our proposed algorithm can obtain the best denoising performance, compared with some typical methods.
ISSN:1047-3203
1095-9076
DOI:10.1016/j.jvcir.2016.09.007