文章基本信息

标题：Combining Total Variation and Nonlocal Means Regularization for Edge Preserving Image Deconvolution
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作者：Binbin Hao ; Jianguang Zhu
期刊名称：ELCVIA: electronic letters on computer vision and image analysis
印刷版ISSN：1577-5097
出版年度：2011
卷号：10
期号：1
语种：English
出版社：Centre de Visió per Computador
摘要：We propose a new edge preserving image deconvolution model by combining total variation and nonlocal means regularization. Natural images exhibit an high degree of redundancy. Using this redundancy, the nonlocal means regularization strategy is a good technique for detail preserving image restoration. In order to further improve the visual quality of the nonlocal means based algorithm, total variation is introduced to the model to better preserve edges. Then an efficient alternating minimization procedure is used to solve the model. Numerical experiments illustrate the effectiveness of the proposed algorithm.
关键词：Image Modelling;Image and Video Processing;Total Variation Regularization;Nonlocal Means Filte;Image Deconvolution