Projection matrix design for co-sparse analysis model based compressive sensing

Research output: Contribution to journalConference articlepeer-review

3 Citations (Scopus)


Co-sparse analysis model based-compressive sensing (CAMBCS) has gained attention in recent years as alternative to conventional sparse synthesis model based (SSMB)-CS. The equivalent operator as counterpart of the equivalent dictionary in the SSMB-CS is introduced in the CAMB-CS as the product of projection matrix and transpose of the analysis dictionary. This paper proposes an algorithm for designing suitable projection matrix for CAMB-CS by minimizing the mutual coherence of the equivalent operator based on equiangular tight frames design. The simulation results show that the CAMB-CS with the proposed projection matrix outperforms the SSMB-CS in terms of the signal quality reconstruction.

Original languageEnglish
Article number01061
JournalMATEC Web of Conferences
Publication statusPublished - 30 Mar 2018
Event2nd International Joint Conference on Advanced Engineering and Technology, IJCAET 2017 and International Symposium on Advanced Mechanical and Power Engineering, ISAMPE 2017 - Bali, Indonesia
Duration: 24 Aug 201726 Aug 2017


Dive into the research topics of 'Projection matrix design for co-sparse analysis model based compressive sensing'. Together they form a unique fingerprint.

Cite this