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Adaptive convergence nonuniformity correction algorithm

机译:自适应收敛非均匀性校正算法

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摘要

Nowadays, convergence and ghosting artifacts are common problems in scene-based nonuniformity correction (NUC) algorithms. In this study, we introduce the idea of space frequency to the scene-based NUC. Then the convergence speed factor is presented, which can adaptively change the convergence speed by a change of the scene dynamic range. In fact, the convergence speed factor role is to decrease the statistical data standard deviation. The nonuniformity space relativity characteristic was summarized by plenty of experimental statistical data. The space relativity characteristic was used to correct the convergence speed factor, which can make it more stable. Finally, real and simulated infrared image sequences were applied to demonstrate the positive effect of our algorithm.
机译:如今,收敛和重影伪影已成为基于场景的非均匀性校正(NUC)算法中的常见问题。在这项研究中,我们将空间频率的概念引入到基于场景的NUC中。然后提出了收敛速度因子,可以通过改变场景动态范围来自适应地改变收敛速度。实际上,收敛速度因子的作用是减少统计数据的标准偏差。大量的实验统计数据总结了非均匀性的空间相对性特征。利用空间相关性特性校正收敛速度因子,可以使其更稳定。最后,通过真实的和模拟的红外图像序列来证明我们算法的积极效果。

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