Compressive sensing reconstruction based on weighted directional total variation

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• 作者：Lihua Min 闵莉花 ; Can Feng 冯 灿
• 关键词：Key wordscompressive sensing ; weighted directional total variation ; majorization ; minimization algorithm
• 刊名：Journal of Shanghai Jiaotong University (Science)
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：22
• 期：1
• 页码：114-120
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Electrical Engineering; Materials Science, general; Computer Science, general; Architecture, general; Life Sciences, general;
• 出版者：Shanghai Jiaotong University Press
• ISSN：1995-8188
• 卷排序：22

文摘

Directionality of image plays a very important role in human visual system and it is important prior information of image. In this paper we propose a weighted directional total variation model to reconstruct image from its finite number of noisy compressive samples. A novel self-adaption, texture preservation method is designed to select the weight. Inspired by majorization-minimization scheme, we develop an efficient algorithm to seek the optimal solution of the proposed model by minimizing a sequence of quadratic surrogate penalties. The numerical examples are performed to compare its performance with four state-of-the-art algorithms. Experimental results clearly show that our method has better reconstruction accuracy on texture images than the existing scheme.