Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1993 · 13 citations · 1 references
Natural SciencesVision ScienceComputer VisionRemote SensingSensorsInfrared Remote SensingMaximum Likelihood EstimationRemote Sensing ImageryInfrared SensingObjectsPoint SourcesSignal-to-noise AnalysisSignal-to-noise AssessmentSuper-resolution ImagingSuper-resolution ProcessingSuper-resolution Reconstruction
This is a description of a model-based maximum likelihood estimation technique for determining the position and intensities of closely spaced objects (CSO's) present in the focal plane of a forward-looking infrared (FLIR) sensor. The object model considered here is approximate point sources; we present a methodology to superresolve two point sources separated closer than the Rayleigh resolution criteria. The Cramer-Rao theoretical lower bound is derived in closed form; the variance of the proposed estimator will be compared to this bound to verify its superresolving capability. Simulation results are presented for medium and high signal-to-noise (Gaussian noise) ratios and source separations.
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