Alternative continuous- and discrete-time neural networks for image restoration. (2nd October 2019)

• Record Type: Journal Article
• Authors: Li, Yawei; Gao, Xingbao
• Is Part Of: Network. Volume 30:Number 1/4(2019)
• Page Start: 107
• Page End: 124
• Publication Date: 2019-10-02
• Subjects: Neural network -- image restoration -- convergence -- stability; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; 006.32
• DOI: 10.1080/0954898X.2019.1677955
• Languages: English
• ISSNs: 0954-898X

Abstract

ABSTRACT: This paper presents alternative continuous- and discrete-time neural networks for image restoration in real time by introducing new vectors and transforming its optimization conditions into a system of double projection equations. The proposed neural networks are shown to be stable in the sense of Lyapunov and convergent for any starting point. Compared with the existing neural networks for image restoration, the proposed models have the least neurons, a one-layer structure and the faster convergence, and is suitable to parallel implementation. The validity and transient behaviour of the proposed neural network is demonstrated by numerical examples.