  We describe a method for recovering the irradiance underlying a collection of
images corrupted by atmospheric turbulence. Since supervised data is often
technically impossible to obtain, assumptions and biases have to be imposed to
solve this inverse problem, and we choose to model them explicitly. Rather than
initializing a latent irradiance ("template") by heuristics to estimate
deformation, we select one of the images as a reference, and model the
deformation in this image by the aggregation of the optical flow from it to
other images, exploiting a prior imposed by Central Limit Theorem. Then with a
novel flow inversion module, the model registers each image TO the template but
WITHOUT the template, avoiding artifacts related to poor template
initialization. To illustrate the robustness of the method, we simply (i)
select the first frame as the reference and (ii) use the simplest optical flow
to estimate the warpings, yet the improvement in registration is decisive in
the final reconstruction, as we achieve state-of-the-art performance despite
its simplicity. The method establishes a strong baseline that can be further
improved by integrating it seamlessly into more sophisticated pipelines, or
with domain-specific methods if so desired.
