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Fast Adaptive Nonuniformity Correction for Infrared Focal-Plane Array Detectors

Abstract

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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Correspondence to Esteban Vera.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Vera, E., Torres, S. Fast Adaptive Nonuniformity Correction for Infrared Focal-Plane Array Detectors. EURASIP J. Adv. Signal Process. 2005, 560759 (2005). https://doi.org/10.1155/ASP.2005.1994

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Keywords and phrases

  • infrared detectors
  • focal-plane array
  • nonuniformity correction
  • fixed-pattern noise
  • neural networks
  • least mean square