Learning to restore images degraded by atmospheric turbulence using uncertainty

TL;DR

This paper introduces a deep learning method that uses uncertainty estimation to effectively restore images degraded by atmospheric turbulence, improving image quality in long-range imaging scenarios.

Contribution

It presents a novel approach combining Monte Carlo dropout-based uncertainty estimation with deep learning for atmospheric turbulence image restoration.

Findings

Effective restoration on synthetic images

Improved real-world image quality

Uncertainty maps guide the restoration process

Abstract

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