Hyperparameter estimation for satellite image restoration using a MCMC maximum-likelihood method
✍ Scribed by André Jalobeanu; Laure Blanc-Féraud; Josiane Zerubia
- Publisher
- Elsevier Science
- Year
- 2002
- Tongue
- English
- Weight
- 502 KB
- Volume
- 35
- Category
- Article
- ISSN
- 0031-3203
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✦ Synopsis
The satellite image deconvolution problem is ill-posed and must be regularized. Herein, we use an edge-preserving regularization model using a 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 (MLE), 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 modi"ed Geman}Yang algorithm, using an auxiliary variable and a cosine transform. We present a Markov chain Monte Carlo maximum-likelihood (MCMCML) technique which is able to simultaneously achieve the estimation and the reconstruction.