上海交通大学学报（英文版） ›› 2017, Vol. 22 ›› Issue (1): 114-120.doi: 10.1007/s12204-017-1809-5

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Compressive Sensing Reconstruction Based on Weighted Directional Total Variation

MIN Lihua1*(闵莉花), FENG Can2 (冯 灿)   

  1. (1. School of Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; 2. Department of Beidou, North Information Control Research Academy Group Co., Ltd., Nanjing 211153, China)
  • 出版日期:2017-02-28 发布日期:2017-04-04
  • 通讯作者: MIN Lihua1*(闵莉花) E-mail:mlh@njupt.edu.cn

Compressive Sensing Reconstruction Based on Weighted Directional Total Variation

MIN Lihua1*(闵莉花), FENG Can2 (冯 灿)   

  1. (1. School of Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; 2. Department of Beidou, North Information Control Research Academy Group Co., Ltd., Nanjing 211153, China)
  • Online:2017-02-28 Published:2017-04-04
  • Contact: MIN Lihua1*(闵莉花) E-mail:mlh@njupt.edu.cn

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.

关键词: compressive sensing, weighted directional total variation, majorization-minimization algorithm

Abstract: 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.

Key words: compressive sensing, weighted directional total variation, majorization-minimization algorithm

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