Geometric correction of atmospheric turbulence-degraded video containing moving objects.
Geometric correction of atmospheric turbulence-degraded video containing moving objects.
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DOI：
10.1364/oe.23.005091
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发表时间：
2015-02
期刊：
影响因子：
3.8
通讯作者：
K. Halder;M. Tahtali;S. Anavatti
中科院分区：
文献类型：
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作者：
K. Halder;M. Tahtali;S. Anavatti
远距离监视是一项具有挑战性的任务，因为大气湍流会导致随时间变化的图像偏移和图像模糊。随着成像距离的增加，这些失真变得更加显著。本文提出了一种新的方法来补偿视频序列中的图像偏移，同时保持视频中的真实的运动对象不受伤害。该方法首先采用高精度、快速的光流技术估计输入帧的运动矢量图，然后采用质心算法生成无运动物体的几何正确帧。第二步涉及应用用于检测视频序列中的真实的移动对象的算法，然后将其恢复为不受影响的那些对象。所提出的方法的性能进行了验证，通过比较它与一个国家的最先进的方法。仿真实验表明，该方法在保持运动目标的同时，显著提高了图像恢复的精度。
Long-distance surveillance is a challenging task because of atmospheric turbulence that causes time-varying image shifts and blurs in images. These distortions become more significant as the imaging distance increases. This paper presents a new method for compensating image shifting in a video sequence while keeping real moving objects in the video unharmed. In this approach, firstly, a highly accurate and fast optical flow technique is applied to estimate the motion vector maps of the input frames and a centroid algorithm is employed to generate a geometrically correct frame in which there is no moving object. The second step involves applying an algorithm for detecting real moving objects in the video sequence and then restoring it with those objects unaffected. The performance of the proposed method is verified by comparing it with that of a state-of-the-art approach. Simulation experiments using both synthetic and real-life surveillance videos demonstrate that this method significantly improves the accuracy of image restoration while preserving moving objects.