高阶SVD和全变差正则的乘性噪声去除模型

doi:10.3969/j.issn.1001-2400.2016.03.014

Abstract

Abstract:

Smoothness, sparsity and self-similarity are the priors widely used in image denoising due to their importance in representing natural images. Motivated by the collaborative roles of higher order singular value decomposition and total variation regularization, a new approach that can simultaneously capture the above priors is proposed in this paper for removing the multiplicative noises. By taking advantages of local adaptiveness, sparsity and self-similarity realized by higher order singular value decomposition, the proposed approach starts with similar-patch-group-wise adaptive denoising on the log-transformed image, followed by the iterative optimization implemented by the total variation constraint which considers the prior of smoothness. Experiments demonstrate the advantages of the proposed approach in removing multiplicative noise and preserving the details near the edges and in the texture area.

Key words: higher order singular value decomposition, multiplicative noise, total variation, nonlocal filter, image denoising

CLC Number:

HUO Leigang;FENG Xiangchu;WANG Xudong;HUO Chunlei. Higherorder singular value decomposition- and total variation- regularized multiplicative noise removal model[J].Journal of Xidian University, 2016, 43(3): 78-84.

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Higherorder singular value decomposition- and total variation- regularized multiplicative noise removal model

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