Rethinking Atmospheric Turbulence Mitigation

Understand

State-of-the-art atmospheric turbulence image restoration methods utilize standard image processing tools such as optical flow, lucky region and blind deconvolution to restore the images.

• While promising results have been reported over the past decade, many of the methods are agnostic to the physical model that generates the distortion.
• In this paper, we revisit the turbulence restoration problem by analyzing the reference frame generation and the blind deconvolution steps in a typical restoration pipeline.
• By leveraging tools in large deviation theory, we rigorously prove the minimum number of frames required to generate a reliable reference for both static and dynamic scenes.