Preconditioned generalized orthogonal matching pursuit

EURASIP journal on advances in signal processing

article

Tong, Zhishen¹  Wang, Feng³  Hu, Chenyu¹  Wang, Jian⁴  Han, Shensheng¹ 

[1] Key Laboratory for Quantum Optics and Center for Cold Atom Physics of CAS, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences;Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences;Department of Management, Shanghai Business School;School of Data Science, Fudan University;ZJLab, Fudan-Xinzailing Joint Research Centre for Big Data, Shanghai Key Lab of Intelligent Information Processing;Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences

关键词: Compressed sensing;    Preconditioning;    Generalized orthogonal matching pursuit;    Ghost imaging;    Mutual coherence;   

DOI  :  10.1186/s13634-020-00680-9

来源: SpringerOpen

【 摘 要 】

Recently, compressed sensing (CS) has aroused much attention for that sparse signals can be retrieved from a small set of linear samples. Algorithms for CS reconstruction can be roughly classified into two categories: (1) optimization-based algorithms and (2) greedy search ones. In this paper, we propose an algorithm called the preconditioned generalized orthogonal matching pursuit (Pre-gOMP) to promote the recovery performance. We provide a sufficient condition for exact recovery via the Pre-gOMP algorithm, which says that if the mutual coherence of the preconditioned sampling matrix Φ satisfies $ \mu ({\Phi }) 1) is the number of indices selected in each iteration of Pre-gOMP. We also apply the Pre-gOMP algorithm to the application of ghost imaging. Our experimental results demonstrate that the Pre-gOMP can largely improve the imaging quality of ghost imaging, while boosting the imaging speed.

【 授权许可】

CC BY