A TV Based Restoration Model with Local Constraints. Almansa, A., Ballester, C., Caselles, V., & Haro, G. Journal of Scientific Computing, 34(3):209-236, Springer Netherlands, 10, 2007.
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.
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 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},
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 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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