|
Title
|
|
|
|
Wavelet-based EM algorithm for multispectral-image restoration
| |
|
Author
|
|
|
|
| |
|
Abstract
|
|
|
|
| In this paper, we present a technique for the restoration of multispectral images. The presented procedure is based on an expectation-maximization (EM) algorithm, which applies iteratively a deconvolution and a denoising step. The restoration is performed in a multispectral way instead of band-by-band. The deconvolution technique is a generalization of the EM-based grayscale-image restoration and allows for the reconstruction of spatial as well as spectral blurring. The denoising step is performed in wavelet domain. To account for interband correlations, a multispectral probability density model for the wavelet coefficients is chosen. Rather than using a multinormal model, we opted for a Gaussian scale mixture model, which is a heavy-tailed model. Also in this paper, the framework is extended to include an auxiliary image of the same scene to improve the restoration. Experiments on Landsat and AVIRIS multispectral remote-sensing images are conducted. |
| |
|
Language
|
|
|
|
English
| |
|
Source (journal)
|
|
|
|
IEEE transactions on geoscience and remote sensing / Institute of Electrical and Electronics Engineers. - New York, N.Y., 1980, currens
| |
|
Publication
|
|
|
|
New York, N.Y.
:
2009
| |
|
ISSN
|
|
|
|
0196-2892
[print]
1558-0644
[online]
| |
|
DOI
|
|
|
|
10.1109/TGRS.2009.2031103
| |
|
Volume/pages
|
|
|
|
47
:11
(2009)
, p. 3892-3898
| |
|
ISI
|
|
|
|
000271140400029
| |
|
Full text (Publisher's DOI)
|
|
|
|
| |
|