论文标题
带有复杂值卷积神经网络的大气湍流去除
Atmospheric Turbulence Removal with Complex-Valued Convolutional Neural Network
论文作者
论文摘要
大气湍流扭曲了视觉图像，对于人类和机器的信息解释总是有问题的。消除大气湍流失真的大多数发达的方法都是基于模型的。但是，这些方法需要高计算和大量内存，使实时操作不可行。因此，基于深度学习的方法已引起了更多的关注，但目前仅在静态场景上有效地工作。本文提出了一个基于学习的新型框架，提供短暂的时间跨度以支持动态场景。我们利用复合物价值的卷积为相信息，随着大气湍流的改变，比使用普通的现实价值卷积更好。提出了两个串联模块。第一个模块旨在删除几何扭曲，如果记忆足够，则将第二个模块应用于完善视频的微观详细信息。实验结果表明，我们提出的框架有效地减轻了大气湍流失真，并显着优于现有方法。
Atmospheric turbulence distorts visual imagery and is always problematic for information interpretation by both human and machine. Most well-developed approaches to remove atmospheric turbulence distortion are model-based. However, these methods require high computation and large memory making real-time operation infeasible. Deep learning-based approaches have hence gained more attention but currently work efficiently only on static scenes. This paper presents a novel learning-based framework offering short temporal spanning to support dynamic scenes. We exploit complex-valued convolutions as phase information, altered by atmospheric turbulence, is captured better than using ordinary real-valued convolutions. Two concatenated modules are proposed. The first module aims to remove geometric distortions and, if enough memory, the second module is applied to refine micro details of the videos. Experimental results show that our proposed framework efficiently mitigates the atmospheric turbulence distortion and significantly outperforms existing methods.
