Blind deconvolution of images using neural networks
Ronald J. Steriti, Michael A. Fiddy · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1994
In this paper we consider the blind deconvolution of an image from an unknown blurring function using a technique employing two nested Hopfield neural networks. This iterative method consists of two steps, first estimating the blurring function followed by the use of this function to estimate the original image. The successive inter-linked energy minimizations are found to converge in practice although a convergence proof has not yet been established.