Scheduled System Maintenance on May 29th, 2015:
IEEE Xplore will be upgraded between 11:00 AM and 10:00 PM EDT. During this time there may be intermittent impact on performance. We apologize for any inconvenience.
By Topic

Wavelet-Based EM Algorithm for Multispectral-Image Restoration

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Duijster, A. ; Dept. of Phys., Univ. of Antwerp, Wilrijk, Belgium ; Scheunders, P. ; De Backer, S.

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.

Published in:

Geoscience and Remote Sensing, IEEE Transactions on  (Volume:47 ,  Issue: 11 )