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A new nonlocal means based framework for mixed noise removal
Jiang,Jielin1,2,3; Yang,Kang1; Yang,Jian4; Yang,Zhi Xin2; Chen,Yadang1; Luo,Lei4
2021-03-28
Source PublicationNeurocomputing
ISSN0925-2312
Volume431Pages:57-68
AbstractMany image-denoising approaches seek to remove either additive white Gaussian noise (AWGN) or impulse noise (IN), because both types are easier to process when considered separately. However, images can be corrupted by a mixture of AWGN and IN during image acquisition and transmission. The major difficulty of mixed noise removal arises through the complex distribution of noise, which cannot be fitted by a simple parametric model. In this paper, a new nonlocal means based framework (NMF) is proposed. A median-type filter is used to detect the locations of outlier pixels; these pixels are then replaced by their nonlocal means, which makes the mixed noise distribution approximately Gaussian. To prove the effectiveness of our NMF, a low rank approximation combined with NMF (LRNM) model is presented for mixed noise removal. In the LRNM, we group similar nonlocal patches in a matrix and apply a low rank approximation to reconstruct the clean image. Gradient regularization is added to better preserve the image texture details. A convolutional neural network (CNN) combined with the NMF (NMF-CNN) is also presented, to prove the generality of the NMF. Experimental results show that LRNM and NMF-CNN achieve a strong mixed noise removal performance and also produce visually pleasing denoising results.
KeywordConvolutional neural network Gradient regularization Low rank approximation Mixed noise removal Nonlocal self-similarity
DOI10.1016/j.neucom.2020.12.039
URLView the original
Language英语
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Document TypeJournal article
CollectionUniversity of Macau
Affiliation1.School of Computer and Software,Jiangsu Engineering Center of Network Monitoring,Nanjing University of Information Science and Technology,Nanjing,China
2.State Key Laboratory of Internet of Things for Smart City and Department of Electromechanical Engineering,University of Macau,Macau,China
3.Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET),Nanjing University of Information Science and Technology,Nanjing,China
4.School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing,China
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Jiang,Jielin,Yang,Kang,Yang,Jian,et al. A new nonlocal means based framework for mixed noise removal[J]. Neurocomputing,2021,431:57-68.
APA Jiang,Jielin,Yang,Kang,Yang,Jian,Yang,Zhi Xin,Chen,Yadang,&Luo,Lei.(2021).A new nonlocal means based framework for mixed noise removal.Neurocomputing,431,57-68.
MLA Jiang,Jielin,et al."A new nonlocal means based framework for mixed noise removal".Neurocomputing 431(2021):57-68.
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