This paper proposes an original inhomogeneous restoration (deconvolution) model under the Bayesian framework. In this model, regularization is achieved, during the iterative restoration process, with an adaptive segmentation-based regularization term whose goal is to apply local smoothness constraints on estimated constant areas of the image to be recovered. To this end, the parameters of this restoration a priori model relies on an unsupervised Markovian over-segmentation. To compute the MAP estimate associated to the restoration, we use a simple steepest descent procedure resulting in an efficient iterative process converging to a globally optimal restoration. The experiments reported in this paper demonstrate that the discussed method performs competitively and sometimes better than the best existing state-of-the-art methods in benchmark tests.


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    Title :

    An adaptive segmentation-based regularization term for image restoration


    Contributors:


    Publication date :

    2005-01-01


    Size :

    201579 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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