Open Access

Fast Adaptive Nonuniformity Correction for Infrared Focal-Plane Array Detectors

EURASIP Journal on Advances in Signal Processing20052005:560759

Received: 29 October 2003

Published: 15 August 2005


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.

Keywords and phrases

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

Authors’ Affiliations

Department of Electrical Engineering, University of Concepcion


© Vera and Torres 2005