Low storage space for compressive sensing: semi-tensor product approach

Low storage space for compressive sensing: semi-tensor product approach

Abstract Random measurement matrices play a critical role in successful recovery with the compressive sensing (CS) framework. However, due to its randomly generated elements, these matrices require massive amounts of storage space to implement a random matrix in CS applications. To effectively reduc...

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Bibliographic Details
Main Authors: Jinming Wang, Shiping Ye, Yue Ruan, Chaoxiang Chen
Format: Article
Language:English
Published: SpringerOpen 2017-07-01
Series:EURASIP Journal on Image and Video Processing
Subjects:
Compressed sensing
Compressive sensing
Random measurement matrix
Storage space
Semi-tensor product
Online Access:http://link.springer.com/article/10.1186/s13640-017-0199-9

Internet

http://link.springer.com/article/10.1186/s13640-017-0199-9

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