Monte Carlo-Based Restoration of Images Degraded by Atmospheric Turbulence

Cong Wang, Guodong Sun, Cailing Wang, Zixuan Gao, Hongwei Wang

Abstract: Atmospheric turbulence can often introduce phase errors into a propagating light field, thus resulting in anisoplanatic and temporally varying blur and distortion of images. Restoring such images degraded by atmospheric turbulence is extremely ill-posed, due to multiple plausible solutions for a given input image. Most methods offer a deterministic estimation of clean images and require high-computational costs. To address these challenges, this article proposes a fast turbulence mitigation network (FTMNet). It is a lightweight model for atmospheric turbulence mitigation. Differing other methods, it does not employ a strategy for producing a single deterministic reconstruction. Instead, it leverages the Monte Carlo method to enhance restoration performance and produces a different and reasonable set of reconstructed images for a given input. As a result, FTMNet effectively mitigates atmospheric turbulence effect while maintaining low-inference time and computational resource requirements. Experimental results demonstrate that FTMNet shows high-inference speed, reaching 90 fps, and outperforms the state-of-the-art peers.