Learning To Restore Images Degraded By Atmospheric Turbulence Using Uncertainty

Atmospheric turbulence can significantly degrade the quality of images acquired by long-range imaging systems by caus- ing spatially and temporally random fluctuations in the index of refraction of the atmosphere. Variations in the refractive in- dex 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 net- work 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.