Discrete Random Signals and Statistical Signal Processing
Discrete Random Signals and Statistical Signal Processing
Fast adaptive nonuniformity correction for infrared focal-plane array detectors
EURASIP Journal on Applied Signal Processing
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
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
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In this paper, we proposed a model based correlation measure between gain and offset nonuniformity for infrared focal plane array (FPA) imaging systems. Actually, several nonuniformity correction methods perform correction of nonuniformities by means of gain and off-set estimation in a detector-by-detector basis using several approach such as laboratory calibration methods, registration-based algorithm, and algebraic and statistical scene-based algorithm. Some statistical algorithms model the slow and random drift in time that the gain and offset present in many practical FPA applications by means of Gauss-Markov model, assuming that the gain and offset are uncorrelated. Due to this, in this work we present a study and model of such correlation by means of a generalized Gauss-Markov model. The gain and offset model-based correlation is validate using several infrared video sequences.