  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.
