  Atmospheric Turbulence (AT) correction is a challenging restoration task as
it consists of two distortions: geometric distortion and spatially variant
blur. Diffusion models have shown impressive accomplishments in photo-realistic
image synthesis and beyond. In this paper, we propose a novel deep conditional
diffusion model under a variational inference framework to solve the AT
correction problem. We use this framework to improve performance by learning
latent prior information from the input and degradation processes. We use the
learned information to further condition the diffusion model. Experiments are
conducted in a comprehensive synthetic AT dataset. We show that the proposed
framework achieves good quantitative and qualitative results.
