Image restoration using Modified Recurrent Hopfield Neural Network
S. Uma, S. Annadurai · International Journal of Signal and Imaging Systems Engineering · 2008
An approach to restore an image degraded by a blur function and corrupted by random noise is proposed using a Modified Recurrent Hopfield neural network (MRHNN) model. The existing Hopfield network takes more iteration to converge and results in poor quality images as the noise level increases. In the proposed network a fraction of the output of a neuron is fed only to higher order neurons resulting in reduced number of iterations as well as a better SNR. Two updating algorithms: the sequential update; n-simultaneous update are used with the proposed network.