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

CORC > 软件研究所 > 软件所图书馆 > 期刊论文

	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
刊名	Applied Soft Computing Journal
	2011
卷号	12 期号:2 页码:-
关键词	Frequency response Image processing Image retrieval Noise pollution control Vectors
ISSN号	1568-4946
中文摘要	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.
英文摘要	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.
学科主题	Computer Science
收录类别	EI ; SCI
资助信息	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
公开日期	2013-10-08
内容类型	期刊论文
源URL	[http://ir.iscas.ac.cn/handle/311060/16066]
专题	软件研究所_软件所图书馆_期刊论文
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):-.