Low-complexity compressive sensing with downsampling

IEICE Electronics Express

Dongeun Lee²  Jaesik Choi¹  Heonshik Shin² 

[1] School of Electrical and Computer Engineering, Ulsan National Institute of Science and Technology;School of Computer Science and Engineering, Seoul National University

关键词: compressive sensing;    downsampling;    sparse random matrix;    low-complexity;    sparse signal recovery;   

DOI  :  10.1587/elex.11.20130947

学科分类：电子、光学、磁材料

来源: Denshi Jouhou Tsuushin Gakkai

【 摘 要 】

References(9)Compressive sensing (CS) with sparse random matrix for the random sensing basis reduces source coding complexity of sensing devices. We propose a downsampling scheme to this framework in order to further reduce the complexity and improve coding efficiency simultaneously. As a result, our scheme can deliver significant gains to a wide variety of resource-constrained sensors. Experimental results show that the computational complexity decreases by 99.95% compared to other CS framework with dense random measurements. Furthermore, bit-rate can be saved up to 46.29%, by which less bandwidth is consumed.

【 授权许可】

Unknown