An adaptive edge-preserving image denoising technique using patch-based weighted-SVD filtering in wavelet domain

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• 作者：Paras Jain ; Vipin Tyagi
• 关键词：Wavelet transform ; Singular value decomposition (SVD) ; Edge detection ; Adaptive filtering
• 刊名：Multimedia Tools and Applications
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：76
• 期：2
• 页码：1659-1679
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems;
• 出版者：Springer US
• ISSN：1573-7721
• 卷排序：76

文摘

Image denoising has always been one of the standard problems in image processing and computer vision. It is always recommendable for a denoising method to preserve important image features, such as edges, corners, etc., during its execution. Image denoising methods based on wavelet transforms have been shown their excellence in providing an efficient edge-preserving image denoising, because they provide a suitable basis for separating noisy signal from the image signal. This paper presents a novel edge-preserving image denoising technique based on wavelet transforms. The wavelet domain representation of the noisy image is obtained through its multi-level decomposition into wavelet coefficients by applying a discrete wavelet transform. A patch-based weighted-SVD filtering technique is used to effectively reduce noise while preserving important features of the original image. Experimental results, compared to other approaches, demonstrate that the proposed method achieves very impressive gain in denoising performance.