Real-World Atmospheric Turbulence Correction via Domain Adaptation
Xijun Wang Santiago López-Tapia Aggelos K. Katsaggelos
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.