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 Publication | IEEE Access
![]() |
ISSN | 2169-3536 |
2020 | |
Volume | 8Pages:5009-5021 |
Indexed By | SCI ; EI |
EI Accession number | 20200508096416 |
WOS ID | WOS:000549786500005 |
Contribution Rank | 1 |
Funding Organization | National Natural Science Foundation of China under Grant 61773087 ; Fundamental Research Funds for Central Universities under Grant DUT18RC(6)005 ; State Key Laboratory of Robotics |
Keyword | Uncertain 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 |
WOS Subject | Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications |
WOS Keyword | SUPPORT VECTOR REGRESSION ; CONTOURLET TRANSFORM ; WAVELET ; ALGORITHM ; SELECTION ; MACHINES ; NETWORKS ; REMOVAL ; DESIGN ; SIGNAL |
WOS Research Area | Computer Science ; Engineering ; Telecommunications |
Funding Project | National Natural Science Foundation of China[61773087] ; Fundamental Research Funds for Central Universities[DUT18RC(6)005] ; State Key Laboratory of Robotics |
Citation statistics | |
Document Type | 期刊论文 |
Identifier | http://ir.sia.cn/handle/173321/26226 |
Collection | 机器人学研究室 |
Corresponding Author | Lyu, Zhiyu; Lyu, Zhiyu |
Affiliation | 1.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 3.Department of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China 4.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,et al. 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,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. |
Files in This Item: | ||||||
File Name/Size | DocType | Version | Access | License | ||
Denoising of Uncerta(9131KB) | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Application Full Text |
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment