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Supervised and Unsupervised Subband Adaptive Denoising Frameworks with Polynomial Threshold Function
Gong TR(宫铁瑞); Yang ZJ(杨志家); Wang GS(王庚善); Jiao P(焦平)
Department工业控制网络与系统研究室
Source PublicationMathematical Problems in Engineering
ISSN1024-123X
2017
Volume2017Pages:1-12
Indexed BySCI ; EI
EI Accession number20171303490993
WOS IDWOS:000398481000001
Contribution Rank1
Funding OrganizationNational Science and Technology Major Project of the Ministry of Science and Technology of China (no. Y6D8020801).
AbstractUnlike inflexible structure of soft and hard threshold function, a unified linear matrix form with flexible structure for threshold function is proposed. Based on the unified linear flexible structure threshold function, both supervised and unsupervised subband adaptive denoising frameworks are established. To determine flexible coefficients, a direct mean-square error (MSE) minimization is conducted in supervised denoising while Stein's unbiased risk estimate as a MSE estimate is minimized in unsupervised denoising. The SURE rule requires no hypotheses or a priori knowledge about clean signals. Furthermore, we discuss conditions to obtain optimal coefficients for both supervised and unsupervised subband adaptive denoising frameworks. Applying an Odd-Term Reserving Polynomial (OTRP) function as concrete threshold function, simulations for polynomial order, denoising performance, and noise effect are conducted. Proper polynomial order and noise effect are analyzed. Both proposed methods are compared with soft and hard based denoising technologies - VisuShrink, SureShrink, MiniMaxShrink, and BayesShrink - in denoising performance simulation. Results show that the proposed approaches perform better in both MSE and signal-to-noise ratio (SNR) sense.
Language英语
WOS HeadingsScience & Technology ; Technology ; Physical Sciences
WOS SubjectEngineering, Multidisciplinary ; Mathematics, Interdisciplinary Applications
WOS KeywordWAVELET SHRINKAGE ; NOISE ESTIMATION ; DOMAIN ; DISTRIBUTIONS
WOS Research AreaEngineering ; Mathematics
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/20295
Collection工业控制网络与系统研究室
Corresponding AuthorGong TR(宫铁瑞)
Affiliation1.Department of Industrial Control Networks and Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.University of Chinese Academy of Sciences, Beijing 100049, China
Recommended Citation
GB/T 7714
Gong TR,Yang ZJ,Wang GS,et al. Supervised and Unsupervised Subband Adaptive Denoising Frameworks with Polynomial Threshold Function[J]. Mathematical Problems in Engineering,2017,2017:1-12.
APA Gong TR,Yang ZJ,Wang GS,&Jiao P.(2017).Supervised and Unsupervised Subband Adaptive Denoising Frameworks with Polynomial Threshold Function.Mathematical Problems in Engineering,2017,1-12.
MLA Gong TR,et al."Supervised and Unsupervised Subband Adaptive Denoising Frameworks with Polynomial Threshold Function".Mathematical Problems in Engineering 2017(2017):1-12.
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