A novel adaptive scene-based nonuniformity correction technique is presented. The technique simultaneously estimates detector parameters and performs the nonuniformity correction based on the retina-like neural network approach. The proposed method includes the use of an adaptive learning rate rule in the gain and offset parameter estimation process. This learning rate rule, together with a reduction in the averaging window size used for the parameter estimation, may provide an efficient implementation that should increase the original method's scene-based ability to estimate the fixed-pattern noise. The performance of the proposed algorithm is then evaluated with infrared image sequences with simulated and real fixed-pattern noise. The results show a significative faster and more reliable fixed-pattern noise reduction, tracking the parameters drift, and presenting a good adaptability to scene changes and nonuniformity conditions.

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adaptive learning rate rule scene changes retinalike neural network detector parameters nonuniformity conditions methods scenebased gain and offset parameter estimation fixedpattern noise reduction tracking algorithm averaging window infrared image sequences with simulated and real fixedpattern adaptive scenebased nonuniformity correction technique

Sergio Torres,Esteban Vera,.Fast Adaptive Nonuniformity Correction for Infrared Focal-Plane Array Detectors. 2005 (13),.

Sergio Torres,Esteban Vera,"Fast Adaptive Nonuniformity Correction for Infrared Focal-Plane Array Detectors" 2005

Sergio Torres,Esteban Vera,"Fast Adaptive Nonuniformity Correction for Infrared Focal-Plane Array Detectors"