Real time stabilization of long range observation system turbulent video
Real time stabilization of long range observation system turbulent video
复制标题
远距离观测系统湍流视频实时稳定
DOI：
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
A. Shtern
中科院分区：
文献类型：
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作者：
B. Fishbain;L. Yaroslavsky;I. Ideses;Ofer Ben;A. Shtern
远程观测系统是一个领域，它在许多领域，如天文学（即行星探索），地质学，生态学，交通管制，遥感和国土安全（监视和军事情报）中引起了很多兴趣。理想情况下，图像质量将仅受所使用的光学设置的限制，但是，在这样的系统中，图像失真的主要原因是大气湍流。本文提出了一种实时补偿算法，该算法能够在保证视频中真实的运动目标不受影响的前提下，对视频序列中由于大气湍流引起的图像失真进行补偿。该算法基于运动目标的提取，因此湍流失真补偿只适用于图像的静态区域。为此，提出了一种分层决策机制。首先，一个轻量级的计算决策机制，提取最稳定的地区。然后第二步通过更复杂的计算算法来提高精度。最后，输入帧中被标记为静止的所有区域都被静止场景的估计所取代。恢复的视频表现出良好的稳定性，静止的物体，同时保留真实的运动。这在标准计算机硬件上实时实现。
Long Range Observation Systems is a domain, which carries a lot of interest in many fields such as astronomy (i.e. planet exploration), geology, ecology, traffic control, remote sensing, and homeland security (surveillance and military intelligence). Ideally, image quality would be limited only by the optical setup used, but, in such systems, the major cause for image distortion is atmospheric turbulence. The paper presents a real-time algorithm that compensates images distortion due to atmospheric turbulence in video sequences, while keeping the real moving objects in the video unharmed. The algorithm is based on moving objects extraction; hence turbulence distortion compensation is applied only to the static areas of images. For that purpose a hierarchical decision mechanism is suggested. First, a lightweight computational decision mechanism which extracts most stationary areas is applied. Then a second step improves accuracy by more computationally complex algorithms. Finally, all areas in the incoming frame that were tagged as stationary are replaced with an estimation of the stationary scene. The restored videos exhibit excellent stability for stationary objects while retaining real motion. This is achieved in real-time on standard computer hardware.
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DOI：
10.1007/978-1-4939-7647-8_1
发表时间：
2018
期刊：
Neuromethods
影响因子：
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作者：
Joshi,AnandA
通讯作者：
Joshi,AnandA