固定模式噪声
探测器
偏移量(计算机科学)
计算机科学
噪音(视频)
人工智能
人工神经网络
算法
还原(数学)
跟踪(教育)
降噪
过程(计算)
计算机视觉
模式识别(心理学)
数学
图像(数学)
像素
电信
操作系统
几何学
教育学
心理学
程序设计语言
作者
Esteban Vera,Sergio N. Torres
标识
DOI:10.1155/asp.2005.1994
摘要
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.
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