Theoretical and experimental analyses of restoring degraded images based on continuous Hopfield neural networks

This paper proposes a modified full parallel self-feedback continuous Hopfield neural network model to restore degraded images. Theoretical analyses show that this model is able to ensure its energy converging to the global minimum more precisely, therefore good restored images are obtained. The result of this model on restoring uniform velocity motion-blurred images is compared with the Paik and Katsaggelos (1992) method. Experimental results indicate that the SNR(signal-to-noise ratio) of the images restored from our model are improved obviously and the visual quality of them are quite good.