  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
