Adaptive signal processing
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Nonuniformity correction of infrared image sequences using the constant-statistics constraint
IEEE Transactions on Image Processing
Projection-based image registration in the presence of fixed-pattern noise
IEEE Transactions on Image Processing
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
A RLS filter for nonuniformity and ghosting correction of infrared image sequences
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
A recursive least square adaptive filter for nonuniformity correction of infrared image sequences
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
A neural network for nonuniformity and ghosting correction of infrared image sequences
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
“De-Ghosting” artifact in scene-based nonuniformity correction of infrared image sequences
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Improved neural networks based method for infrared focal plane arrays nonuniformity correction
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
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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.