Reconstruction for block-based compressive sensing of image with reweighted double sparse constraint

EURASIP Journal on Image and Video Processing

Zhiyong Huang¹  Yuanhong Zhong¹  Xinyu Cheng¹  Guan Huang¹  Jing Zhang¹  Zhaokun Zhou¹ 

[1] School of Microelectronics and Communication Engineering, Chongqing University;

关键词: Image reconstruction;    Compressive sensing;    Reweighted double sparse constraint;   

DOI  :  10.1186/s13640-019-0464-1

来源: DOAJ

【 摘 要 】

Abstract Block compressive sensing reduces the computational complexity by dividing the image into multiple patches for processing, but the performance of the reconstruction algorithm is decreased. Generally, the reconstruction algorithm improves the quality of reconstructed image by adding various constraints and regularization terms, namely prior information. In this paper, a reweighted double sparse constraint reconstruction model which combines the residual sparsity and ℓ1 regularization term is proposed. The residual sparsity aims to exploit the nonlocal similarity of image patches, and the ℓ1 regularization term is used to utilize the local sparsity of image patches. The resulting model is solved under the frame of split Bregman iteration (SBI). A large number of experiments show that the algorithm in this paper can reconstruct the original image efficiently and is comparable to the current representative compressive sensing reconstruction algorithm.

【 授权许可】

Unknown