Wavelet-Based Multispectral Image Restoration

In this paper, restoration of multispectral images is performed. The presented procedure is based on an Expectation-Maximization algorithm, which applies iteratively a deconvolution 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 using a multispectral approach instead of a bandwise one. To account for interband correlations, a multispectral probability density model for the wavelet coefficients is chosen. Furthermore, 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.