Multimodel Kalman filtering for adaptive nonuniformity correction in infrared sensors
JOSA A, Vol. 23, Issue 6, pp. 1282-1291 (2006)
http://dx.doi.org/10.1364/JOSAA.23.001282
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Abstract
We present an adaptive technique for the estimation of nonuniformity parameters of infrared focal-plane arrays that is robust with respect to changes and uncertainties in scene and sensor characteristics. The proposed algorithm is based on using a bank of Kalman filters in parallel. Each filter independently estimates state variables comprising the gain and the bias matrices of the sensor, according to its own dynamic-model parameters. The supervising component of the algorithm then generates the final estimates of the state variables by forming a weighted superposition of all the estimates rendered by each Kalman filter. The weights are computed and updated iteratively, according to the a posteriori-likelihood principle. The performance of the estimator and its ability to compensate for fixed-pattern noise is tested using both simulated and real data obtained from two cameras operating in the mid- and long-wave infrared regime.
© 2006 Optical Society of America
OCIS Codes
(040.1520) Detectors : CCD, charge-coupled device
(100.2550) Image processing : Focal-plane-array image processors
(100.3010) Image processing : Image reconstruction techniques
(100.3020) Image processing : Image reconstruction-restoration
(110.3080) Imaging systems : Infrared imaging
ToC Category:
Atmospheric and Oceanic Optics
History
Original Manuscript: July 29, 2005
Manuscript Accepted: November 18, 2005
Citation
Jorge E. Pezoa, Majeed M. Hayat, Sergio N. Torres, and Md. Saifur Rahman, "Multimodel Kalman filtering for adaptive nonuniformity correction in infrared sensors," J. Opt. Soc. Am. A 23, 1282-1291 (2006)
http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-23-6-1282
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- Statistical Algorithm for Nonuniformity Correction in Focal-Plane Arrays (AO)
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