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Hyperparameter estimation for satellite image restoration using a MCMC maximum-likelihood method. (English) Zbl 0993.68136

Summary: The satellite image deconvolution problem is ill-posed and must be regularized. Herein, we use an edge-preserving regularization model using a \(\varphi\) function, involving two hyperparameters. Our goal is to estimate the optimal parameters in order to automatically reconstruct images. We propose to use the maximum-likelihood estimator, applied to the observed image. We need sampling from prior and posterior distributions. Since the convolution prevents use of standard samplers, we have developed a modified Geman-Yang algorithm, using an auxiliary variable and a cosine transform. We present a Markov chain Monte Carlo maximum-likelihood technique which is able to simultaneously achieve the estimation and the reconstruction.

MSC:

68U10 Computing methodologies for image processing
68T10 Pattern recognition, speech recognition
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