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High-resolution infrared image reconstruction using multiple randomly shifted low-resolution aliased frames

Forward looking infrared (FLIR) detector arrays generally produce spatially undersampled images because the FLIR arrays can not be made dense enough to yield a sufficiently high spatial sampling frequency. Multi-frame techniques, such as microscanning, are an effective means of reducing aliasing and increasing resolution in images produced by staring imaging systems. These techniques involve interlacing a set of image frames that have been shifted with respect to each other during acquisition. The FLIR system is mounted on a moving platform, such as an aircraft, and the vibrations associated with the platform are used to generate the shifts. Since a fixed number of image frames is required, and the shifts are random, the acquired frames will not fall on a uniformly spaced grid. In this paper, we utilize gradient based shift registration to estimate the shifts between the acquired frames and then use a weighted nearest-neighbor approach for placing the frames onto a uniform grid to form a final high resolution image. We apply a Wiener filter to the high-resolution image in order to remove blurring due to the detector and optics of the imaging system. Simulation and experimental results are presented to verify the effectiveness of the proposed technique. The methods used here are significantly faster than alternate techniques, and are found to be especially suitable for real time applications.