Sensor expansion through integration of microscanning and super-resolution algorithms
The spatial resolution of undersampled (aliased) or diffraction limited images can be improved through microscanning and super-resolution technologies. The objective of this Air Force Phase II Small Business innovative Research (SBIR) was to develop and demonstrate real-time or near real-time microscanning 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 factor of two. A new microscanning algorithm was developed and demonstrated that can reliably detect less than one fifth of an IFOV displacement using field test data. i The integration of the super-resolution and microscanning algorithms was demonstrated and resolution gains of four to six times can be achieved if the image is undersampled by a factor of two or three. Consequently, it makes sense to use a wide undersampled FOV sensor in which high spatial resolution can be obtained as desired using microscanning and super-resolution technologies.© 2000 SPIE.
Proceedings of SPIE - The International Society for Optical Engineering
Sensor expansion through integration of microscanning and super-resolution algorithms.
Proceedings of SPIE - The International Society for Optical Engineering,
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