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Bayesian Filtering and Some Markovian Random Fields for Image Restoration

Abstract : The present work introduces an alternative method to deal with digital image restoration into a Bayesian framework, particularly, the use of a new half-quadratic function is proposed. The Bayesian methodology is based on the prior knowledge of some information that allows an efficient modelling of the image acquisition process. The edge preservation of objects into the image while smoothing noise is necessary in an adequate model. Thus, we use a convexity criteria given by a semi-Huber function to obtain adequate weighting of the cost functions (half-quadratic) to be minimized. A comparison between the new introduced scheme and other three existent schemes, for the cases of noise ¯ltering and image deblurring, is presented. Results showed a satisfactory performance and the effectiveness of the proposed estimator.
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Contributor : Karine El Rassi <>
Submitted on : Monday, November 21, 2011 - 11:59:34 AM
Last modification on : Thursday, March 29, 2018 - 11:06:05 AM


  • HAL Id : hal-00643152, version 1



José Ismael de la Rosa Vargas, Jesus Villa, Efren Gonzalez, Ma Araiza, Osvaldo Gutierrez, et al.. Bayesian Filtering and Some Markovian Random Fields for Image Restoration. IEEE International Reunion de Otono de Potencia Electronica y Computacion (ROPEC'11), Nov 2011, Morelia Michoacan, Mexico. pp.209-215. ⟨hal-00643152⟩



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