Compressive sensing reconstruction based on weighted directional total variation

被引：2

作者：

Min L. ^{[1
]}

Feng C. ^{[2
]}

机构：

[1] School of Science, Nanjing University of Posts and Telecommunications, Nanjing

[2] Department of Beidou, North Information Control Research Academy Group Co., Ltd., Nanjing

来源：

Journal of Shanghai Jiaotong University (Science) | 2017年 / 22卷 / 1期

关键词：

compressive sensing; majorization-minimization algorithm; weighted directional total variation;

D O I：

10.1007/s12204-017-1809-5

中图分类号：

学科分类号：

摘要：

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. © 2017, Shanghai Jiaotong University and Springer-Verlag Berlin Heidelberg.

引用

页码：114 / 120

页数：6

共 16 条

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