LLp norm regularization based group sparse representation for image compressed sensing recovery
作者：
Highlights：
• Nonconvex norm regularization has been proposed for CS image recovery via GSR.
• An efficient algorithm based on SBI and MM has been developed to solve GSR- .
• RGSR- has been proposed to cope with impulsive noisy CS measurements.
• It has been shown that GSR- and RGSR- outperform other CS recovery methods.
• GSR- effectively recovers images from low number of CS measurements.
摘要
•Nonconvex norm regularization has been proposed for CS image recovery via GSR.•An efficient algorithm based on SBI and MM has been developed to solve GSR- .•RGSR- has been proposed to cope with impulsive noisy CS measurements.•It has been shown that GSR- and RGSR- outperform other CS recovery methods.•GSR- effectively recovers images from low number of CS measurements.
论文关键词：Compressed sensing,Group sparse representation,Half-quadratic theory,Image recovery,Nonlocal sparsity
论文评审过程：Received 15 March 2019, Revised 23 June 2019, Accepted 24 July 2019, Available online 31 July 2019, Version of Record 20 August 2019.
论文官网地址：https://doi.org/10.1016/j.image.2019.07.021
