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.
An adaptive segmentation-based regularization term for image restoration
2005-01-01
201579 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
An Adaptive Segmentation-Based Regularization Term for Image Restoration
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