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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.