Image restoration using chaotic simulated annealing
Leipo Yan, Lipo Wang · 2004
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.