期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 37, 期 10, 页码 7040-7049出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2010.03.014
关键词
Image denoising; Undecimated discrete wavelet transform; LS-SVM; Spatial regularity; Adaptive threshold
类别
资金
- National Natural Science Foundation of China [60773031, 60873222]
- Open Foundation of State Key Laboratory of Networking and Switching Technology of China [SKLNST-2008-1-01]
- Open Foundation of State Key Laboratory of Information Security of China [03-06]
- Open Foundation of State Key Laboratory for Novel Software Technology of China [A200702]
- Liaoning Research Project for Institutions of Higher Education of China [2008351]
Image denoising is an important image processing task, both as itself, and as a preprocessing in image processing pipeline. The least squares support vector machine (LS-SVM) has shown to exhibit excellent classification performance in many applications. Based on undecimated discrete wavelet transform, a new wavelet-based image denoising using LS-SVM is proposed in this paper. Firstly, the noisy image is decomposed into different subbands of frequency and orientation responses using the undecimated discrete wavelet transform. Secondly, the feature vector for a pixel in a noisy image is formed by the spatial regularity in wavelet domain, and the LS-SVM model is obtained by training. Then the wavelet coefficients are divided into two classes (noisy coefficients and noise-free ones) by LS-SVM training model. Finally, all noisy wavelet coefficients are relatively well denoised by shrink method, in which the adaptive threshold is utilized. 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. (C) 2010 Elsevier Ltd. All rights reserved.
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