1 August 2006 Maximum a posteriori image and seeing condition estimation from partially coherent two-dimensional light detection and ranging images
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Optical Engineering, 45(8), 086201 (2006). doi:10.1117/1.2338572
Abstract
Recent developments in staring focal plane technology have spawned significant interest in the application of gated laser radar systems to battlefield remote sensing. Such environments are characterized by rapidly changing atmospheric seeing conditions and significant image distortion caused by long slant-range paths through the most dense regions of the atmosphere. Limited weight, space, and computational resources tend to prohibit the application of adaptive optic systems to mitigate atmospheric image distortion. We demonstrate and validate the use of a fast, iterative, maximum a posteriori (MAP) estimator to estimate both the original target scene and the ensemble-averaged atmospheric optical transfer function parameterized by Fried's seeing parameter. Wide-field-of-view sensor data is simulated to emulate images collected on an experimental test range. Simulated and experimental multiframe motion-compensated average images are deconvolved by the MAP estimator to produce most likely estimates of the truth image as well as the atmospheric seeing condition. For comparison, Fried's seeing parameter is estimated from experimentally collected images using a knife-edge response technique. The MAP estimator is found to yield seeing condition estimates within approximately 6% using simulated speckle images, and within approximately 8% of knife-edge derived truth for a limited set of experimentally collected image data.
Adam MacDonald, Stephen C. Cain, Ernest E. Armstrong, "Maximum a posteriori image and seeing condition estimation from partially coherent two-dimensional light detection and ranging images," Optical Engineering 45(8), 086201 (1 August 2006). http://dx.doi.org/10.1117/1.2338572
JOURNAL ARTICLE
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KEYWORDS
Speckle

Image processing

Atmospheric optics

Atmospheric propagation

Sensors

Atmospheric modeling

LIDAR

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