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题名:
Supervised and Unsupervised Subband Adaptive Denoising Frameworks with Polynomial Threshold Function
作者: Gong TR(宫铁瑞); Yang ZJ(杨志家); Wang GS(王庚善); Jiao P(焦平)
作者部门: 工业控制网络与系统研究室
通讯作者: 宫铁瑞
刊名: Mathematical Problems in Engineering
ISSN号: 1024-123X
出版日期: 2017
卷号: 2017, 页码:1-12
收录类别: SCI ; EI
产权排序: 1
项目资助者: National Science and Technology Major Project of the Ministry of Science and Technology of China (no. Y6D8020801).
摘要: Unlike 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.
语种: 英语
EI收录号: 20171303490993
WOS记录号: WOS:000398481000001
WOS标题词: Science & Technology ; Technology ; Physical Sciences
类目[WOS]: Engineering, Multidisciplinary ; Mathematics, Interdisciplinary Applications
关键词[WOS]: WAVELET SHRINKAGE ; NOISE ESTIMATION ; DOMAIN ; DISTRIBUTIONS
研究领域[WOS]: Engineering ; Mathematics
Citation statistics:
内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/20295
Appears in Collections:工业控制网络与系统研究室_期刊论文

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Recommended Citation:
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.
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