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.

翻译：远距离成像中的大气湍流因空间与时间维度的随机变化，会显著降低捕获场景的质量与保真度。这种畸变在从监控到天文观测等多种应用场景中均构成严峻挑战，亟需鲁棒的抑制策略。基于模型的方法虽能取得良好效果，但其处理速度极为缓慢。深度学习方法在图像与视频复原领域展现出潜力，却难以有效处理此类时空变化的畸变。本文提出一种结合几何复原与增强模块的新框架。通过采用具备可变形三维卷积的金字塔结构消除随机扰动与几何畸变，从而获得对齐的帧序列。随后利用三维Swin Transformers的多尺度架构，基于这些对齐帧重建出清晰锐利的图像。该框架在合成与真实大气湍流效应场景中均展现出超越现有技术的性能表现，同时具备合理的处理速度与模型规模。