Open AccessRobust optimisation algorithm for the measurement matrix in compressed sensing
Author(s) -
Zhou Ying,
Sun Quansen,
Liu Jixin
Publication year - 2018
Publication title -
caai transactions on intelligence technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.613
H-Index - 15
ISSN - 2468-2322
DOI - 10.1049/trit.2018.1011
Subject(s) - compressed sensing , mutual coherence , hadamard transform , matrix (chemical analysis) , algorithm , hadamard matrix , computer science , diagonal , energy (signal processing) , efficient energy use , coherence (philosophical gambling strategy) , mathematical optimization , engineering , mathematics , materials science , composite material , mathematical analysis , statistics , geometry , electrical engineering
The measurement matrix which plays an important role in compressed sensing has got a lot of attention. However, the existing measurement matrices ignore the energy concentration characteristic of the natural images in the sparse domain, which can help to improve the sensing efficiency and the construction efficiency. Here, the authors propose a simple but efficient measurement matrix based on the Hadamard matrix, named Hadamard‐diagonal matrix (HDM). In HDM, the energy conservation in the sparse domain is maximised. In addition, considering the reconstruction performance can be further improved by decreasing the mutual coherence of the measurement matrix, an effective optimisation strategy is adopted in order to reducing the mutual coherence for better reconstruction quality. The authors conduct several experiments to evaluate the performance of HDM and the effectiveness of optimisation algorithm. The experimental results show that HDM performs better than other popular measurement matrices, and the optimisation algorithm can improve the performance of not only the HDM but also the other popular measurement matrices.