Learning to restore images degraded by atmospheric turbulence using uncertainty
This work addresses image quality issues for long-range imaging systems, such as surveillance or astronomy, but is incremental as it builds on existing deep learning methods with uncertainty estimation.
The paper tackles the problem of restoring images degraded by atmospheric turbulence, which causes geometric distortions and blur, by proposing a deep learning approach that uses epistemic uncertainty from Monte Carlo dropouts to guide restoration, achieving improved results on synthetic and real images.
Atmospheric turbulence can significantly degrade the quality of images acquired by long-range imaging systems by causing spatially and temporally random fluctuations in the index of refraction of the atmosphere. Variations in the refractive index causes the captured images to be geometrically distorted and blurry. Hence, it is important to compensate for the visual degradation in images caused by atmospheric turbulence. In this paper, we propose a deep learning-based approach for restring a single image degraded by atmospheric turbulence. We make use of the epistemic uncertainty based on Monte Carlo dropouts to capture regions in the image where the network is having hard time restoring. The estimated uncertainty maps are then used to guide the network to obtain the restored image. Extensive experiments are conducted on synthetic and real images to show the significance of the proposed work. Code is available at : https://github.com/rajeevyasarla/AT-Net