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17 July 2000 Kalman-filtering approach for nonuniformity correction in focal plane array sensors
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A Kalman filter is developed to estimate the temporal drift in the gain and the offset of detectors in focal-plane array sensors from scene data. The novelty of this approach is that the gain and the offset are modeled by random sequences (state variables) which must be estimated from the current and past noisy scene data. The gain and the offset are assumed constant over fixed-length blocks of frames; however, these parameters may slowly drift from block to block according to a temporal discrete-time Gauss-Markov process. The input to the Kalman filter consists of a sequence of blocks of frames and the output at any time is a vector containing current estimates of the bias and the offset for each detector. Once these estimates are generated, the true image is restored by means of a least- mean-square error temporal FIR filter. The efficacy of the reported technique is demonstrated by applying it to two sets of real infrared data and the advantage rendered by the Gauss-Markov model is shown.
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Sergio N. Torres, Majeed M. Hayat, Ernest E. Armstrong, and Brian J. Yasuda "Kalman-filtering approach for nonuniformity correction in focal plane array sensors", Proc. SPIE 4030, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XI, (17 July 2000);

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