Deep learning-based turbulence mitigation for long-range imaging

The distortion caused by turbulence in the atmosphere during long range imaging can result in low quality images and videos. This, in turn, greatly increases the difficulty of any post acquisition tasks such as tracking or classification. The mitigation of such distortions is therefore important, allowing any post processing steps to be performed successfully. We make use of the EDVR network, initially designed for video restoration and super resolution, to mitigate the effects of turbulence. This paper presents two modifications to the training and architecture of EDVR, that improve its applicability to turbulence mitigation: namely the replacement of the deformable convolution layers present in the original EDVR architecture, alongside the addition of perceptual loss. This paper also presents an analysis of common metrics used for image quality assessment and it evaluates their suitability for the comparison of turbulence mitigation approaches. In this context, traditional metrics such as Peak Signal-to-Noise Ratio can be misleading, as they could reward undesirable attributes, such as increased contrast instead of high frequency detail. We argue that the applications for which turbulence mitigated imagery is used should be the real markers of quality for any turbulence mitigation technique. To aid in this, we also present a new turbulence classification dataset that can be used to measure the classification performance before
and after turbulence mitigation.