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Deterministic constructions of compressed sensing matrices based on optimal codebooks and codes
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DOI：10.1016/j.amc.2018.09.042.png)
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Abstract
En 中文
Compressed sensing theory provides a new approach to acquire data as a sampling technique and makes sure that a sparse signal can be reconstructed from few measurements. The construction of compressed sensing matrices is a main problem in compressed sensing theory. In this paper, the deterministic compressed sensing matrices are provided using optimal codebooks and codes. Using specific linear and nonlinear codes, we present deterministic constructions of compressed sensing matrices, which are generalizations of DeVore's construction and Li et al.'s construction. Compared with DeVore's matrices and Li et al.'s matrices, by using appropriate optimal codebooks and specific codes, the compressed sensing matrices we construct are superior to DeVore's matrices and Li et al.'s matrices. (C) 2018 Elsevier Inc. All rights reserved.
Keywords:
Compressed sensing
Coherence
Sparsity
Restricted isometry property
Optimal codebooks
Codes
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