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Restauration automatique d'images satellitaires par une méthode MCMC
| Content Provider | Semantic Scholar |
|---|---|
| Author | Jalobeanu, André Blanc-Féraud, Laure Zerubia, Josiane |
| Copyright Year | 1999 |
| Abstract | The problem presented herein is the reconstruction of blurred and noisy satellite images. Image degradations are supposed to be known. We use a regularizing model based on a '-function, avoiding noise ampli cation while preserving the edges of the solution. This variational model has two hyperparameters, which are automatically estimated. This is achieved by the Maximum Likelihood estimator, applied on the observed image. We have developed a MCMC estimation algorithm, which uses a sampling method inspired from [6]. We use a cosine transform instead of the Fourier transform. We propose a new and fast reconstruction algorithm, derived from the sampling method, which ables to simultaneously deblur the degraded image and to estimate the hyperparameters. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://documents.irevues.inist.fr/bitstream/handle/2042/12942/ARTI1259.pdf?sequence=1 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
