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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Spatially Adaptive Regularization Image Restoration Using a Modified Hopfield Network


    Contributors:


    Publication date :

    2007-09-01


    Size :

    691404 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Adaptive regularization in image restoration by unsupervised learning

    Wong, H.-S. / Guan, L. | British Library Online Contents | 1998



    An Adaptive Segmentation-Based Regularization Term for Image Restoration

    Mignotte, M. | British Library Conference Proceedings | 2005


    Optimization of regularization operators for adaptive least squares image restoration

    Bundschuh, B. O. / Russian Academy of Sciences / Samara State Aerospace University et al. | British Library Conference Proceedings | 1994


    A Hopfield neural network for adaptive control

    MEARS, M. / SMITH, R. / CHANDLER, P. et al. | AIAA | 1993