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Instrument parameter estimation in bayesian convex deconvolution

Abstract : This paper proposes a Bayesian approach for estimation of instrument parameter in convex image deconvolution. The parameters of the instrument response (PSF) are jointly estimated with the image leading to a myopic deconvolution approach. In addition a special convex field allowing efficient hyperparameter estimation is used. The solution is based on a global a posteriori law for unknown parameters and object. The estimate is chosen in the sense of the posterior mean, numerically calculated by means of a Monte-Carlo Markov chain algorithm. The computation is efficient with a partial implementation in Fourier space. Simulation results are provided to assess the effectiveness of the proposed approach.
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François Orieux, Thomas Rodet, Jean-François Giovannelli. Instrument parameter estimation in bayesian convex deconvolution. IEEE International Conference on Image Processing (ICIP), Sep 2010, Hong Kong, Hong Kong SAR China. pp.1161 - 1164, ⟨10.1109/ICIP.2010.5651917⟩. ⟨hal-00546604⟩



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