4 August 2009 Scene-based nonuniformity correction with motion detection
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Scene-based nonuniformity correction algorithms are widely concerned since they only need the readout infrared data captured by the imaging system during its normal operation. However, there still exists the problem of ghosting artifact in the algorithms, and their performance is noticeably degraded when the methods are applied over scenes with lack of motion. In order to solve this problem, a novel adaptive scene-based NUC algorithm, with a design of foreground and background, is presented. As the foreground, the neural network, using the adaptive learning rate rule, performs the normal NUC. As the background, the block-based motion detection monitors changes of the scene and determines the way of parameter update. The strength of the algorithm lies in its simplicity and low computational complexity. The performance of the proposed algorithm is then evaluated with infrared image sequences with simulated and real fixedpattern noise. The results show a more reliable fixed-pattern noise reduction, tracking the parameter drift, and presenting a good adaptability to scene changes.
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Xin-gang Mou, Xin-gang Mou, Gui-lin Zhang, Gui-lin Zhang, Ruo-lan Hu, Ruo-lan Hu, Hang Li, Hang Li, } "Scene-based nonuniformity correction with motion detection", Proc. SPIE 7383, International Symposium on Photoelectronic Detection and Imaging 2009: Advances in Infrared Imaging and Applications, 73833G (4 August 2009); doi: 10.1117/12.835734; https://doi.org/10.1117/12.835734

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