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21 August 2001 Sensor expansion through integration of microscanning and superresolution algorithms
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The spatial resolution of under sampled or diffraction limited images can be improved through micro scanning and super-resolution technologies. The objective of this Air Force Phase Ii Small Business Innovative Research was to develop and demonstrate real-time or near real-time micro scanning and super-resolution algorithms using passive millimeter wave imagery. A new super-resolution algorithm based on expectation-maximization was developed which is insensitive to missing data, incorporates both positivity and smoothness constraints, and rapidly converges in 15 to 20 iterations. Analysis using measured data shows that the practical resolution gain that can be expected using this algorithm is less than a facto of two. A new micro scanning algorithm was developed and demonstrated that can reliably detect less than one fifth of an IFOV displacement using field test data. The iteration of the super-resolution and microscanning algorithms was demonstrated and resolution gains of four to six times can be achieved if the image is under sampled by a factor of two or three. Consequently, it makes sense to use a wide under sampled FOV sensor in which high spatial resolution can be obtained as desired using micro scanning and super-resolution techniques.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
William R. Reynolds, Denise Talcott, Michael C. Roggemann, John W. Hilgers, and Timothy J. Schulz "Sensor expansion through integration of microscanning and superresolution algorithms", Proc. SPIE 4373, Passive Millimeter-Wave Imaging Technology V, (21 August 2001);

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