a new image denoising scheme using support vector machine classification in shiftable complex directional pyramid domain

ISCAS OpenIR

	a new image denoising scheme using support vector machine classification in shiftable complex directional pyramid domain
	Yang Hong-Ying; Wang Xiang-Yang; Fu Zhong-Kai
	2011
发表期刊	Applied Soft Computing Journal
ISSN	1568-4946
卷号	12 期号:2 页码:-
摘要	Edge-preserving image denoising has become a very intensive research topic. In this paper, we propose a new image denoising scheme using support vector machine (SVM) classification in shiftable complex directional pyramid (PDTDFB) domain. Firstly, the noisy image is decomposed into different subbands of frequency and orientation responses using a PDTDFB transform. Secondly, the feature vector for a pixel in a noisy image is formed by the spatial regularity in PDTDFB domain, and the least squares support vector machine (LS-SVM) model is obtained by training. Then the PDTDFB detail coefficients are divided into two classes (edge-related coefficients and noise-related ones) by LS-SVM training model. Finally, the detail subbands of PDTDFB coefficients are denoised by using the different parameters to control the multiscale and multidirectional anisotropic diffusion. Extensive experimental results demonstrate that our method can obtain better performances in terms of both subjective and objective evaluations than those state-of-the-art denoising techniques. Especially, the proposed method can preserve edges very well while removing noise. © 2011 Elsevier B.V. All rights reserved.; Edge-preserving image denoising has become a very intensive research topic. In this paper, we propose a new image denoising scheme using support vector machine (SVM) classification in shiftable complex directional pyramid (PDTDFB) domain. Firstly, the noisy image is decomposed into different subbands of frequency and orientation responses using a PDTDFB transform. Secondly, the feature vector for a pixel in a noisy image is formed by the spatial regularity in PDTDFB domain, and the least squares support vector machine (LS-SVM) model is obtained by training. Then the PDTDFB detail coefficients are divided into two classes (edge-related coefficients and noise-related ones) by LS-SVM training model. Finally, the detail subbands of PDTDFB coefficients are denoised by using the different parameters to control the multiscale and multidirectional anisotropic diffusion. Extensive experimental results demonstrate that our method can obtain better performances in terms of both subjective and objective evaluations than those state-of-the-art denoising techniques. Especially, the proposed method can preserve edges very well while removing noise. © 2011 Elsevier B.V. All rights reserved.
收录类别	EI ; SCI
关键词	Frequency Response Image Processing Image Retrieval Noise Pollution Control Vectors
部门归属	(1) School of Computer and Information Technology Liaoning Normal University Dalian 116029 China; (2) State Key Laboratory of Information Security Institute of Software Chinese Academy of Sciences Beijing 100190 China
学科领域	Computer Science
资助者	National Natural Science Foundation of China60773031, 60873222; Open Foundation of State Key Laboratory of Information Security of China04-06-1; Open Foundation of Network and Data Security Key Laboratory of Sichuan Province; Open Foundation of Key Laboratory of Modern Acoustics Nanjing University08-02; Liaoning Research Project for Institutions of Higher Education of China2008351, L2010230
语种	英语
WOS记录号	WOS:000298631400028
引用统计	被引频次：7[WOS] [WOS记录] [WOS相关记录]
内容类型	期刊论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16066
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Yang Hong-Ying,Wang Xiang-Yang,Fu Zhong-Kai. a new image denoising scheme using support vector machine classification in shiftable complex directional pyramid domain[J]. Applied Soft Computing Journal,2011,12(2):-.
APA	Yang Hong-Ying,Wang Xiang-Yang,&Fu Zhong-Kai.(2011).a new image denoising scheme using support vector machine classification in shiftable complex directional pyramid domain.Applied Soft Computing Journal,12(2),-.
MLA	Yang Hong-Ying,et al."a new image denoising scheme using support vector machine classification in shiftable complex directional pyramid domain".Applied Soft Computing Journal 12.2(2011):-.