Spatially Adaptive Regularization Image Restoration Using a Modified Hopfield Network

In this paper we present a technique for localized image regularization using a modified Hopfield neural network (MHNN). The algorithm forms a segmented map of the image and classifies it into several clusters, or regions, and assigns each region a regularization parameter according to its local statistics and the prior knowledge about the image obtained by a Bayesian minimum risk (BMR) restoration method. The image segmentation is performed over the BMR restored image. First, the user selects arbitrarily at least one region, and makes a subjective decision to choose the best estimate from among a set of restored images with different regularization parameter applied to the user-selected region. Then, using this decision the algorithm sets up a perception-based selection of the different regularization parameters for restoring in an adaptive fashion the whole image employing the MHNN computations.