A multiresolution framework for local similarity based image denoising

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• 作者：Nasir Rajpoot^a ; ^{nasir@dcs.warwick.ac.uk}  ; [Author Vitae] ; Irfan Butt^b  ; [Author Vitae]
• 关键词：Image denoising ; Bilateral filtering ; Local image statistics
• 刊名：Pattern Recognition
• 出版年：2012
• 期刊代码：100_00313203
• 类别：cp
• 出版时间：August, 2012
• 卷：45
• 期：8
• 页码：2938-2951
• 文件大小：1898 K

摘要

In this paper, we present a generic framework for denoising of images corrupted with additive white Gaussian noise based on the idea of regional similarity. The proposed framework employs a similarity function using the distance between pixels in a multidimensional feature space, whereby multiple feature maps describing various local regional characteristics can be utilized, giving higher weight to pixels having similar regional characteristics. An extension of the proposed framework into a multiresolution setting using wavelets and scale space is presented. It is shown that the resulting multiresolution multilateral (MRM) filtering algorithm not only eliminates the coarse-grain noise but can also faithfully reconstruct anisotropic features, particularly in the presence of high levels of noise.