  Atmospheric turbulence in long-range imaging significantly degrades the
quality and fidelity of captured scenes due to random variations in both
spatial and temporal dimensions. These distortions present a formidable
challenge across various applications, from surveillance to astronomy,
necessitating robust mitigation strategies. While model-based approaches
achieve good results, they are very slow. Deep learning approaches show promise
in image and video restoration but have struggled to address these
spatiotemporal variant distortions effectively. This paper proposes a new
framework that combines geometric restoration with an enhancement module.
Random perturbations and geometric distortion are removed using a pyramid
architecture with deformable 3D convolutions, resulting in aligned frames.
These frames are then used to reconstruct a sharp, clear image via a
multi-scale architecture of 3D Swin Transformers. The proposed framework
demonstrates superior performance over the state of the art for both synthetic
and real atmospheric turbulence effects, with reasonable speed and model size.
