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 :
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