By Topic

Image restoration using chaotic simulated annealing

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Leipo Yan ; Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore ; Lipo Wang

Both the stochastic chaotic simulated annealing and the deterministic chaotic simulated annealing are used to restore gray level images degraded by a known shift-invariant blur function and additive noise. The neural networks are modeled to represent the image whose gray level function is the simple sum of the neuron state variables. The restoration consists of two stages: parameter estimation and image reconstruction. During the first stage, parameters are estimated by comparing the energy function of the neural network to a constraint error function. The neural networks are then updated. Experiments show that noisy chaotic neural network could get good results in relatively shorter time compared to Hopfield neural network and better results compared to transiently chaotic neural network.

Published in:

Neural Networks, 2003. Proceedings of the International Joint Conference on  (Volume:4 )

Date of Conference:

20-24 July 2003