This thesis develops algorithms and applications for compressive sensing, a topic in signal processing that allows reconstruction of a signal from a limited number of linear combinations of the signal. New algorithms are described for common remote sensing problems including superresolution and fusion of images. The algorithms show superior results in comparison with conventional methods. We describe a method that uses compressive sensing to reduce the size of image databases used for content based image retrieval. The thesis also describes an improved estimator that enhances the performance of Matching Pursuit type algorithms, several variants of which have been developed for compressive sensing recovery.

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