  Atmospheric turbulence poses a challenge for the interpretation and visual
perception of visual imagery due to its distortion effects. Model-based
approaches have been used to address this, but such methods often suffer from
artefacts associated with moving content. Conversely, deep learning based
methods are dependent on large and diverse datasets that may not effectively
represent any specific content. In this paper, we address these problems with a
self-supervised learning method that does not require ground truth. The
proposed method is not dependent on any dataset outside of the single data
sequence being processed but is also able to improve the quality of any input
raw sequences or pre-processed sequences. Specifically, our method is based on
an accelerated Deep Image Prior (DIP), but integrates temporal information
using pixel shuffling and a temporal sliding window. This efficiently learns
spatio-temporal priors leading to a system that effectively mitigates
atmospheric turbulence distortions. The experiments show that our method
improves visual quality results qualitatively and quantitatively.
