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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    An adaptive segmentation-based regularization term for image restoration


    Beteiligte:
    Mignotte, M. (Autor:in)


    Erscheinungsdatum :

    2005-01-01


    Format / Umfang :

    201579 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    An Adaptive Segmentation-Based Regularization Term for Image Restoration

    Mignotte, M. | British Library Conference Proceedings | 2005


    Adaptive regularization in image restoration by unsupervised learning

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


    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



    Fusion of regularization terms for image restoration

    Mignotte, M. | British Library Online Contents | 2010