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| 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 | |
| Source | Applied Soft Computing Journal
 |
| ISSN | 1568-4946 |
| Volume | 12Issue:2Pages:- |
| 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.; 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. |
| Indexed Type | EI ; SCI |
| Keyword | Frequency Response Image Processing Image Retrieval Noise Pollution Control Vectors |
| 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 |
| Subject | Computer Science |
| 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 |
| Language | 英语 |
| WOS ID | WOS:000298631400028 |
| Citation statistics | |
| Content Type | 期刊论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/16066 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation 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):-. |
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