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Denoising of Uncertain Type Noise Images by Spatial Feature Classification in Nonsubsampled Shearlet Transform
Lyu, Zhiyu1,2; Han, Min1; Li DC(李德才)2
Department机器人学研究室
Source PublicationIEEE Access
ISSN2169-3536
2020
Volume8Pages:5009-5021
Indexed ByEI
EI Accession number20200508096416
Contribution Rank1
Funding OrganizationNational Natural Science Foundation of China under Grant 61773087 ; Fundamental Research Funds for Central Universities under Grant DUT18RC(6)005 ; State Key Laboratory of Robotics
KeywordUncertain type noise image denoising nonsubsampled shearlet transform (NSST) spatial feature justi able granularity
Abstract

Most denoising methods are designed to deal standard images with specific type noise, which do not perform well when denoising real noisy images contain uncertain types of noise. However, underwater image is a typical uncertain type noise image. To solve this problem, this paper presents a method using spatial feature classification jointing nonsubsampled shearlet transform (NSST) for denoising uncertain type noise images. Justifiable granule is employed to solve the problem of parameter selection. The raw image was decomposed by using the NSST to get one low frequency subband and several high frequency subbands. Then, the preliminary binary map is built, the binary map is employed to decide whether a coefficient contains spatial feature or not. And we employ justifiable granule to solve the difficulty of parameter selection. The high subbands coefficients are classified into two classes by fuzzy support vector machine classification: the texture class and the noise class. At last, the adaptive Bayesian threshold is used to shrink the coefficients. Simulation results show the proposed method is effective in uncertain type noise images(also have good performance in specific type noise). The method we proposed has been compared with other popular denoising methods and get excellent subjective performance and PSNR improvement.

Language英语
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/26226
Collection机器人学研究室
Corresponding AuthorLyu, Zhiyu
Affiliation1.Department of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China
2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
Recommended Citation
GB/T 7714
Lyu, Zhiyu,Han, Min,Li DC. Denoising of Uncertain Type Noise Images by Spatial Feature Classification in Nonsubsampled Shearlet Transform[J]. IEEE Access,2020,8:5009-5021.
APA Lyu, Zhiyu,Han, Min,&Li DC.(2020).Denoising of Uncertain Type Noise Images by Spatial Feature Classification in Nonsubsampled Shearlet Transform.IEEE Access,8,5009-5021.
MLA Lyu, Zhiyu,et al."Denoising of Uncertain Type Noise Images by Spatial Feature Classification in Nonsubsampled Shearlet Transform".IEEE Access 8(2020):5009-5021.
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