Real-World Atmospheric Turbulence Correction via Domain Adaptation
This addresses the challenge of real-world atmospheric turbulence correction for outdoor vision applications, but it is incremental as it builds on existing domain adaptation techniques.
The paper tackles the problem of correcting atmospheric turbulence in real-world images, where existing methods trained on synthetic data underperform, by proposing a domain adaptation framework that links supervised synthetic correction with unsupervised real-world correction, resulting in improved image quality and downstream vision tasks.
Atmospheric turbulence, a common phenomenon in daily life, is primarily caused by the uneven heating of the Earth's surface. This phenomenon results in distorted and blurred acquired images or videos and can significantly impact downstream vision tasks, particularly those that rely on capturing clear, stable images or videos from outdoor environments, such as accurately detecting or recognizing objects. Therefore, people have proposed ways to simulate atmospheric turbulence and designed effective deep learning-based methods to remove the atmospheric turbulence effect. However, these synthesized turbulent images can not cover all the range of real-world turbulence effects. Though the models have achieved great performance for synthetic scenarios, there always exists a performance drop when applied to real-world cases. Moreover, reducing real-world turbulence is a more challenging task as there are no clean ground truth counterparts provided to the models during training. In this paper, we propose a real-world atmospheric turbulence mitigation model under a domain adaptation framework, which links the supervised simulated atmospheric turbulence correction with the unsupervised real-world atmospheric turbulence correction. We will show our proposed method enhances performance in real-world atmospheric turbulence scenarios, improving both image quality and downstream vision tasks.