title: Discrete-time neural network for fast solving large linear L1 estimation problems and its application to image restoration.
abstract: There is growing interest in solving linear L1 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 L1 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 L1 estimation problems but also needs much less computational time than the related algorithms in solving both linear L1 estimation and image restoration problems.
authors.forename: Youshen
authors.initials: Y
authors.lastname: Xia
authors.forename: Changyin
authors.initials: C
authors.lastname: Sun
authors.forename: Wei Xing
authors.initials: WX
authors.lastname: Zheng
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D016014:Linear Models; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted