  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.
We discuss how a turbulence agnostic model can lead to potential flaws, and how
to configure a simple spatial-temporal non-local weighted averaging method to
generate references. For blind deconvolution, we present a new data-driven
prior by analyzing the distributions of the point spread functions. We
demonstrate how a simple prior can outperform state-of-the-art blind
deconvolution methods.
