Turbulence Mitigation

Atmospheric turbulence mitigation focuses on restoring images and videos degraded by atmospheric distortions, improving the quality of long-range imaging across various applications. Current research emphasizes deep learning approaches, employing architectures like transformers, convolutional neural networks, and diffusion probabilistic models, often incorporating physics-based priors to enhance accuracy and generalization from synthetic to real-world data. A key challenge is bridging the gap between simulated and real-world turbulence effects, leading to efforts in creating more realistic datasets and domain adaptation techniques. Successful mitigation strategies will significantly impact fields like surveillance, astronomy, and autonomous navigation by enabling clearer and more reliable image-based information acquisition.