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Scene-based nonuniformity correction algorithm based on optical flow

机译：基于场景的基于光流的不均匀性校正算法

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摘要

The performance of infrared imaging system is strongly affected by non-uniformity in infrared focal-plane arrays (FPA). In the classical scene-based nonuniformity correction (NUC) method, errors commonly occur resulting from local motion between two frames. In this paper, a novel scene-based NUC method is presented. This method calculates robust optical flow between two adjacent frames to get the velocity vector of each pixel in the current frame. In this way, corresponding to the pixel in the current frame, the location of the pixel in previous frame is known, and then these frames can be locally registered easily. Based on the assumption that any two detectors with the same scene would produce the same output value, minimize the mean square error between two local registered images to get the estimation of each detector's gain and offset. With gain and offset parameters, nonuniformity of infrared imaging system can be corrected. One advantage of this scene-based NUC algorithm is that it can adapt to scene with local motion. The performance of the proposed algorithm is studied with infrared image sequences with simulated nonuniformity and infrared imagery with real nonuniformity. It shows that fixed-pattern noise is reduced efficiently even when the scene include local motion.
机译：红外成像系统的性能受红外焦平面阵列（FPA）中的不均匀性的影响。在基于古典场景的不均匀性校正（NUC）方法中，常见的错误常见于两个帧之间的局部运动产生。本文介绍了一种新颖的基于场景的NUC方法。该方法计算在两个相邻帧之间的鲁棒光流动以获取当前帧中的每个像素的速度向量。以这种方式，对应于当前帧中的像素，已知先前帧中的像素的位置是已知的，然后可以容易地局部地登记这些帧。基于具有相同场景的任何两个检测器产生相同的输出值的假设，最小化两个本地注册图像之间的均方误差，以获得每个检测器的增益和偏移的估计。利用增益和偏移参数，可以校正红外成像系统的不均匀性。基于场景的NUC算法的一个优点是它可以适应局部运动的场景。采用红外图像序列研究了所提出的算法的性能，具有模拟的不均匀性和红外图像，具有真正的不均匀性。它表明，即使场景包括本地运动，也可以有效地减少了固定图案噪声。

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