Sparse Signal Recovery via Generalized Entropy Functions Minimization.
IEEE Transactions on Signal Processing(2019)
摘要
Compressive sensing relies on the sparse prior imposed on the signal of interest to solve the ill-posed recovery problem in an under-determined linear system. The objective function used to enforce the sparse prior information should be both effective and easily optimizable. Motivated by the entropy concept from information theory, in this paper we propose the generalized Shannon entropy function ...
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关键词
Entropy,Minimization,Sensors,Noise measurement,Matching pursuit algorithms,Optimization,Compressed sensing
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