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Statistical recursive filtering for offset nonuniformity estimation in infrared focal-plane-array sensors

机译：统计递归滤波，用于红外焦平面阵列传感器中的偏移非均匀性估计

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摘要

We have developed a recursive estimator for the offset nonuniformity (NU) present in infrared (IR) focal-plane-array (FPA) imaging systems. The estimator is optimal in the least-square error sense and estimates the offset NU using only the collected scene data. Our scene-based nonuniformity correction (NUC) method exploits Kalman's theory in order to estimate a constant in noise. The method is based upon two key assumptions: (i) the input irradiance at each detector is assumed to be a random variable uniformly distributed in a range that is common to all detectors in the IR-FPA; and (ii) the IR data sequences considered are temporally short enough such that the offset NU at each pixel of the array remains constant within the sequence. The proposed algorithm iterates over the frames in a pixel-by-pixel basis and updates two filter parameters for each pixel over the IR-FPA. Additionally, an on-line technique for ghosting artifacts cancellation is introduced to the NUC filter and an index to assess the artifact reduction is developed. The ability of the method to compensate for the offset NU is demonstrated by filtering video sequences from mid- and long-wave IR cameras. (C) 2008 Elsevier B.V. All rights reserved.
机译：我们已经为红外（IR）焦平面阵列（FPA）成像系统中存在的偏移不均匀性（NU）开发了一种递归估计器。估计器在最小二乘误差意义上是最优的，并且仅使用收集的场景数据估计偏移量NU。我们基于场景的非均匀性校正（NUC）方法利用卡尔曼理论来估计噪声常数。该方法基于两个关键假设：（i）假定每个检测器的输入辐照度是在IR-FPA中所有检测器共有的范围内均匀分布的随机变量; （ii）所考虑的IR数据序列在时间上足够短，使得阵列的每个像素处的偏移NU在序列内保持恒定。所提出的算法以逐像素为基础遍历帧，并通过IR-FPA为每个像素更新两个滤波器参数。另外，将用于重影伪影消除的在线技术引入到NUC滤波器，并开发了评估伪影减少的索引。通过对来自中波和长波红外摄像机的视频序列进行滤波，证明了该方法补偿偏移NU的能力。 （C）2008 Elsevier B.V.保留所有权利。

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