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大气流退化视频恢复与反复的GAN (ATVR-GAN)

Bar Ettedgui¹, Yitzhak Yitzhaky²

¹Department of Electrical Engineering, Tel Aviv University, Tel Aviv 69978, Israel.

Sensors (Basel, Switzerland)

|November 14, 2023

概括

大气动荡会扭曲远程太空视频. 一个新的生成对抗网络 (GAN) 通过预测光流量和减少模糊度来有效地恢复视频质量.

科学领域:

光学和光子学在光学和光子学.
计算机视觉计算机视觉
天体物理学天体物理学

背景情况:

大气流 (AT) 在远距离成像过程中会造成显著的光 distortion.
这种扭曲导致严重的几何扭曲和模糊在拍摄的视频.
现有的方法很难完全减轻这些复杂的时空文物.

研究的目的:

开发一种新的深度学习方法,以减轻视频中大气流的影响.
提高受大气扭曲影响的太空视频捕捉的质量.
为了解决几何扭曲和时空模糊的问题.

主要方法:

提出了一个新的大气流视频恢复生成对抗网络 (ATVR-GAN).
使用一次性神经网络 (RNN) 发生器来预测流光流 (OF) 场.
采用一个针对时空扭曲的组合损失函数.

主要成果:

该ATVR-GAN有效地预测了现场乱的光学流.
该网络成功地减少了几何扭曲和时空模糊.
与合成和真实数据上的领先算法相比,表现出优越的性能.

结论:

拟议的ATVR-GAN提供了一个强大的解决方案,用于视频中减轻大气动荡.
这种方法显著提高了远程太空成像的清晰度和质量.
这种方法对在动荡条件下需要高保真视频的应用具有前景.

关键词:

在美国,CNN是CNN.没有了,没有了,没有了.一个RNN RNN 大气流是大气中的流.光学流的光学流量视频恢复视频的恢复

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