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Subject: Computer Science
Title:
a new image denoising scheme using support vector machine classification in shiftable complex directional pyramid domain
Author: Yang Hong-Ying ; Wang Xiang-Yang ; Fu Zhong-Kai
Keyword: Frequency response ; Image processing ; Image retrieval ; Noise pollution control ; Vectors
Source: Applied Soft Computing Journal
Issued Date: 2011
Volume: 12, Issue:2, Pages:-
Indexed Type: EI ; SCI
Department: (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
Sponsorship: 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
Abstract: 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.
English Abstract: 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.
Language: 英语
WOS ID: WOS:000298631400028
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Content Type: 期刊论文
URI: http://ir.iscas.ac.cn/handle/311060/16066
Appears in Collections:软件所图书馆_期刊论文

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Recommended Citation:
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,2011-01-01,12(2):-.
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