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

NeRT: Implicit Neural Representations for General Unsupervised Turbulence Mitigation