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Super-resolution preprocessing of data from undersampled imaging systems for phase diversity

Phase diversity algorithms allow wavefront and an estimate of the scene to be reconstructed from multiple images with a known phase change between measurements. These algorithms rely on sampling requirements that are frequently not met in remote sensing imaging systems. It is demonstrated that super-resolution pre-processing of imagery from undersampled systems can effectively increase the sampling, thereby allowing application of traditional phase diversity algorithms. Experimental results are presented for both a point object and an extended scene.