Atmospheric Turbulence Removal with Video Sequence Deep Visual Priors
This addresses the problem of visual distortion in imagery for applications like surveillance or remote sensing, but it is incremental as it builds on existing Deep Image Prior methods.
The paper tackles atmospheric turbulence distortion in video sequences by proposing a self-supervised learning method that does not require ground truth data, resulting in improved visual quality both qualitatively and quantitatively.
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.