Sparse signal recovery based on nonconvex entropy minimization

We propose a new sparsity-promoting objective function to be used in sparse signal recovery. Specifically, the objective is an entropy function l1 defined on the sparse signal x. Compared to the conventional l1, it is a nonconvex function and the optimization problem can be solved based on the fast...

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Bibliographic Details
Main Authors: Shuai Huang, Tran, Dung N., Tran, Trac D.
Format: Conference Proceeding
Language:English
Subjects:
1 norm
compressive sensing
entropy
image recovery
p norm
sparse signal recovery
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Summary:We propose a new sparsity-promoting objective function to be used in sparse signal recovery. Specifically, the objective is an entropy function l1 defined on the sparse signal x. Compared to the conventional l1, it is a nonconvex function and the optimization problem can be solved based on the fast iterative shrinkage thresholding algorithm (FISTA). Experiments on 1-dimensional sparse signal recovery and 2-dimensional real image recovery show that minimizing lp favors sparse solutions, and that it could recover sparse signals better than the convex l1 norm minimization and the nonconvex l p -norm minimization.
ISSN:2381-8549
DOI:10.1109/ICIP.2016.7533084