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Hyperparameter estimation for satellite image restoration using a MCMC maximum-likelihood method

delete2002-02-01
delete45
PRE
AI
A
A. Jalobeanu
L
Laure Blanc‐Féraud
J
Josiane Zerubia *
DOI：10.1016/S0031-3203(00)00178-3delete
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Abstract

Abstract

En 中文
The satellite image deconvolution problem is ill-posed and must be regularized. Herein, we use an edge-preserving regularization model using a co 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 modified 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. (C) 2001 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Keywords:
regularization
phi-function
hyperparameters
variational model
Markov random field
estimation
sampling
MCMC
maximum-likelihood
satellite images
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Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Pattern Recognition cover
Pattern Recognition
IF:
7.6
Papers:
1.3W
Citations:
4.5W

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