abstract bibtex

Abstract We propose in this paper a total variation based restoration model which incorporates the image acquisition model z=h U+n (where z represents the observed sampled image, U is the ideal undistorted image, h denotes the blurring kernel and n is a white Gaussian noise) as a set of local constraints. These constraints, one for each pixel of the image, express the fact that the variance of the noise can be estimated from the residuals zh U if we use a neighborhood of each pixel. This is motivated by the fact that the usual inclusion of the image acquisition model as a single constraint expressing a bound for the variance of the noise does not give satisfactory results if we wish to simultaneously recover textured regions and obtain a good denoising of the image. We use Uzawas algorithm to minimize the total variation subject to the proposed family of local constraints and we display some experiments using this model.

@article{ id = {4da59629-1db6-3f53-a65a-4bd12219f0ad}, title = {A TV Based Restoration Model with Local Constraints}, type = {article}, year = {2007}, identifiers = {[object Object]}, keywords = {image restoration,satellite images,total variation,variational methods}, created = {2013-05-27T10:26:42.000Z}, pages = {209-236}, volume = {34}, websites = {http://www.springerlink.com/index/10.1007/s10915-007-9160-x,http://link.springer.com/10.1007/s10915-007-9160-x}, month = {10}, publisher = {Springer Netherlands}, day = {16}, accessed = {2014-11-20}, file_attached = {false}, profile_id = {764ad609-ab47-3f3e-acff-e0eb66664fdb}, last_modified = {2014-12-20T12:58:56.000Z}, read = {false}, starred = {false}, authored = {true}, confirmed = {true}, hidden = {false}, citation_key = {Almansa2007a}, abstract = {Abstract We propose in this paper a total variation based restoration model which incorporates the image acquisition model z=h U+n (where z represents the observed sampled image, U is the ideal undistorted image, h denotes the blurring kernel and n is a white Gaussian noise) as a set of local constraints. These constraints, one for each pixel of the image, express the fact that the variance of the noise can be estimated from the residuals zh U if we use a neighborhood of each pixel. This is motivated by the fact that the usual inclusion of the image acquisition model as a single constraint expressing a bound for the variance of the noise does not give satisfactory results if we wish to simultaneously recover textured regions and obtain a good denoising of the image. We use Uzawas algorithm to minimize the total variation subject to the proposed family of local constraints and we display some experiments using this model.}, bibtype = {article}, author = {Almansa, A. and Ballester, C. and Caselles, V. and Haro, G.}, journal = {Journal of Scientific Computing}, number = {3} }

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