Collabrative sparse image fusion with application to pan-sharpening

Recently sparse signal re presentation of image patches was explored to solve the pan-sharpening problem for remote sensing images. Although the proposed sparse reconstruction based methods lead to motivating results, yet none of them has considered the fact that the information contained in different multispectral channels may be mutually correlated. In this paper, we extend the Sparse fusion of Images (SparseFI, pronounced "sparsit") algorithm, proposed by the authors before, to a Jointly Sparse Fusion of Images (J-SparseFI) algorithm by exploiting these possible signal structural correlations between different multispectral channels. This is done by making use of the distributed compressive sensing (DCS) theory that restricts the solution of an unde rdetermined system by considering an ensemble of signals being jointly sparse. The algorithm is validated with UltraCam data. In the final presentation, results with Hys pex data will be presented.