DocumentCode
3009079
Title
A noisy chaotic neural network approach to image denoising
Author
Yan, Leipo ; Wang, Lipo ; Yap, Kim-Kui
Author_Institution
Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore
Volume
2
fYear
2004
fDate
24-27 Oct. 2004
Firstpage
1229
Abstract
This paper presents a new approach to address image denoising based on a new neural network, called noisy chaotic neural network (NCNN). The original Bayesian framework of image denoising is reformulated into a constrained optimization problem using continuous relaxation labeling. The NCNN, which combines the simulated annealing technique with the Hopfield neural network (HNN), is employed to solve the optimization problem. It effectively overcomes the local minima problem which may be incurred by the HNN. The experimental results show that the NCNN could offer good quality solutions.
Keywords
Hopfield neural nets; belief networks; chaos; constraint theory; image denoising; simulated annealing; Bayesian framework; HNN; Hopfield neural network; NCNN; constrained optimization problem; continuous relaxation labeling; image denoising; local minima problem; noisy chaotic neural network; simulated annealing technique; Bayesian methods; Chaos; Computer networks; Concurrent computing; Constraint optimization; Degradation; Image denoising; Image restoration; Neural networks; Stochastic processes;
fLanguage
English
Publisher
ieee
Conference_Titel
Image Processing, 2004. ICIP '04. 2004 International Conference on
ISSN
1522-4880
Print_ISBN
0-7803-8554-3
Type
conf
DOI
10.1109/ICIP.2004.1419527
Filename
1419527
Link To Document