Atmospheric Turbulence

Atmospheric turbulence distorts images and videos by randomly bending and scattering light, hindering accurate object detection, image analysis, and scientific observation. Current research heavily focuses on deep learning methods, employing architectures like transformers, convolutional neural networks (including 3D and deformable variants), and diffusion models, to mitigate these distortions, often incorporating physics-based simulations for improved training and generalization. These advancements are crucial for improving the quality of long-range imaging in diverse fields, from astronomy and surveillance to autonomous navigation and remote sensing, enabling more reliable data acquisition and analysis in challenging atmospheric conditions.