科研之友
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摘要
Compressed sensing theory is a sampling technique which provides a fundamentally new approach to data acquisition and makes sure that a sparse signal can be reconstructed from few measurements by taking full use of sparsity. In this paper, firstly, the deterministic compressed sensing matrices using a sparse optimal compressed sensing matrix and codes are constructed, which is a generalization of Wang et al.'s construction. Then, using specific linear and nonlinear codes, we present deterministic constructions of compressed sensing matrices, which is a generalization of DeVore's construction and Li et al.'s construction. Comparing with DeVore's matrices and Li et al.'s matrices, by using specific codes and an appropriate sparse optimal compressed sensing matrix, the compressed sensing matrices we construct are superior to DeVore's matrices and Li et al.'s matrices.
