NeRT: Implicit Neural Representations for General Unsupervised Turbulence Mitigation

TL;DR

NeRT introduces an implicit neural representation approach for unsupervised turbulence mitigation that effectively reconstructs clean images from distorted inputs, outperforming existing methods across various scenarios and enabling real-world application.

Contribution

NeRT is the first to use implicit neural representations combined with a physically accurate turbulence model for unsupervised image restoration in turbulence scenarios.

Findings

NeRT outperforms state-of-the-art methods in qualitative and quantitative evaluations.

NeRT effectively removes turbulence from real-world environments.

NeRT achieves 48x speedup when integrated into video processing.

Abstract

The atmospheric and water turbulence mitigation problems have emerged as challenging inverse problems in computer vision and optics communities over the years. However, current methods either rely heavily on the quality of the training dataset or fail to generalize over various scenarios, such as static scenes, dynamic scenes, and text reconstructions. We propose a general implicit neural representation for unsupervised atmospheric and water turbulence mitigation (NeRT). NeRT leverages the implicit neural representations and the physically correct tilt-then-blur turbulence model to reconstruct the clean, undistorted image, given only dozens of distorted input images. Moreover, we show that NeRT outperforms the state-of-the-art through various qualitative and quantitative evaluations of atmospheric and water turbulence datasets. Furthermore, we demonstrate the ability of NeRT to eliminate uncontrolled turbulence from real-world environments. Lastly, we incorporate NeRT into continuously captured video sequences and demonstrate $48 \times$ speedup.