期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 23, 期 5, 页码 812-820出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2012.2184800
关键词
Discrete-time neural network; global convergence; image restoration; large linear L-1 estimation problem
类别
资金
- National Natural Science Foundation of China [61179037, 60875085, 61125306, 61034002]
- Australian Research Council
There is growing interest in solving linear L-1 estimation problems for sparsity of the solution and robustness against non-Gaussian noise. This paper proposes a discrete-time neural network which can calculate large linear L-1 estimation problems fast. The proposed neural network has a fixed computational step length and is proved to be globally convergent to an optimal solution. Then, the proposed neural network is efficiently applied to image restoration. Numerical results show that the proposed neural network is not only efficient in solving degenerate problems resulting from the nonunique solutions of the linear L-1 estimation problems but also needs much less computational time than the related algorithms in solving both linear L-1 estimation and image restoration problems.
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