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Scene-based nonuniformity correction algorithm based on optical flow

机译：基于光流的基于场景的非均匀性校正算法

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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）中的不均匀性的强烈影响。在经典的基于场景的非均匀性校正（NUC）方法中，通常会由于两个帧之间的局部运动而产生错误。本文提出了一种新颖的基于场景的NUC方法。该方法计算两个相邻帧之间的鲁棒光流，以获取当前帧中每个像素的速度矢量。这样，对应于当前帧中的像素，已知先前帧中的像素的位置，然后可以容易地在本地注册这些帧。基于这样的假设：具有相同场景的任何两个检测器将产生相同的输出值，请最小化两个本地配准图像之间的均方误差，以估算每个检测器的增益和偏移。利用增益和偏移参数，可以校正红外成像系统的不均匀性。这种基于场景的NUC算法的优点之一是它可以适应具有局部运动的场景。利用模拟不均匀性的红外图像序列和真实不均匀性的红外图像研究了该算法的性能。结果表明，即使场景中包含局部运动，也可以有效降低固定模式噪声。

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