Estimation and optimization based ill-posed inverse restoration using fuzzy logic
Multimedia Tools and Applications
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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.