The critical study of mutual coherence properties on compressive sensing framework for sparse reconstruction performance: Compression vs measurement system

Nur Afny C. Andryani, Kadek Dwi Pradnyana, Dadang Gunawan

Research output: Contribution to journalConference article


Compressive Sensing (CS) framework becomes well known since its ability to recover signal only by using less sampling required by Shanon-Nyquist theorem. The lack of required sampling is no longer constraint for having good reconstruction performance. The load is shifted to the reconstruction procedure instead of the sampling acquisition process. As long as the signal can be guaranteed sparse, the CS based method is able to provide high reconstruction accuracy. One of the CS principle is incoherence property, which can be represented by mutual coherence value. It represents the coherence between the sensing matrix and the sparse base dictionary. The theory said the less coherence between those two parameters, the more precise the reconstruction is. In fact, it is not consistently applied. The research presented on this paper find that, the theory is consistent for reconstruction on compression system, while it is not applied on the reconstruction of measurement system. Other properties are found to be more representative on assigning necessary condition for reconstruction performance on measurement system.

Original languageEnglish
Article number12074
JournalJournal of Physics: Conference Series
Issue number1
Publication statusPublished - 16 Apr 2019
EventInternational Conference on Information System, Computer Science and Engineering 2018, ICONISCSE 2018 - Palembang, Indonesia
Duration: 26 Nov 201827 Nov 2018


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