NeRT: Implicit Neural Representations for General Unsupervised Turbulence Mitigation

Understand

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.