Convergence and parameters setting of continuous hopfield neural networks applied to image restoration problem

Abstract

In image processing, Hopfield neural networks (HNNs) have been successively invested to solve the optimization model of image restoration. However, in the discrete case, HNNs lead to the fluctuation behaviors due to the limitation of the hard limit activators. The continuous model of HNNs generates non feasible solutions owing to the trial-and-error setting values process of the model parameters. To deal with these issues effectively, we suggest a new energy function with appropriate parameters settled by the stability analysis of the network. Performance of our method is demonstrated numerically and visually by several computational tests.