Image Restoration Using Modified Hopfield Fuzzy Regularization Method

This paper addresses one of the primary problems of visual information processing known as image restoration. Image restoration is a challenging task because of its ill-posed inverse nature. A modified Hopfield neural network with fuzzy adaptive regularization is proposed that shows potential to minimize constraint mean square error in order to guarantee the optimized results. Adaptive regularization was achieved by using fuzzy quasi-range edge detector. The visual results along with the statistical measurements of the resultant images are presented in the paper. Improved SNRs show that the fuzzy regularization method is superior to other statistical and neural network methods when used along with the modified Hopfield neural network.