Paper Info
Title
Wavelet-Based Multispectral Image Restoration
Abstract
In this paper, restoration of multispectral images is per- formed. The presented procedure is based on an Expectation- Maximization algorithm, which applies iteratively a decon- volution and a denoising step. The deconvolution step is a Landweber iteration step, while in the denoising step wavelet shrinkage is performed. The restoration is improved by us- ing a multispectral approach instead of a bandwise one. To account for interband correlations, a multispectral probability density model for the wavelet coefficients is chosen. Further- more, an auxiliary coregistered noise-free image of the same scene is used to improve the restoration. Experiments on a Landsat multispectral remote sensing image are conducted.
Year
DOI
Venue
2008
10.1109/IGARSS.2008.4779287
Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International
Keywords
DocType
Volume
deconvolution,expectation-maximisation algorithm,geophysical techniques,image fusion,image restoration,remote sensing,wavelet transforms,expectation-maximization algorithm,landsat multispectral remote sensing image,landweber iteration,image denoising,multispectral probability density model,wavelet transform,wavelet-based multispectral image restoration,expectation-maximization (em),gaussian scale mixture model (gsm),multispectral images,denoising,restoration,expectation maximization,probability density,expectation maximization algorithm,wavelets
Conference
3
ISBN
Citations 
PageRank 
978-1-4244-2808-3
2
0.49
References 
Authors
6
3
Name
Order
Citations
PageRank
Arno Duijster1132.48
Steve De Backer220015.14
Paul Scheunders31190102.87