Restricted Structural Random Matrix for compressive sensing

作者：

Highlights：

• Propose a novel sampling matrix that improves the sampling efficiency without scarifying the democracy.

• Combine partial sampling, multi-images super-resolution, coded imaging, and compressive sensing.

• Proposed matrix satisfies the Restricted Isometry Property with competitive reconstruction performance.

摘要

•Propose a novel sampling matrix that improves the sampling efficiency without scarifying the democracy.•Combine partial sampling, multi-images super-resolution, coded imaging, and compressive sensing.•Proposed matrix satisfies the Restricted Isometry Property with competitive reconstruction performance.

论文关键词：Compressive sensing,Structural sparse matrix,Restricted isometry property,Security,Kronecker compressive sensing

论文评审过程：Received 10 February 2020, Revised 24 September 2020, Accepted 5 October 2020, Available online 8 October 2020, Version of Record 9 October 2020.

论文官网地址：https://doi.org/10.1016/j.image.2020.116017