  In this technical report, we present the solution developed by our team
VIELab-HUST for text recognition through atmospheric turbulence in Track 2.1 of
the CVPR 2023 UG$^{2}$+ challenge. Our solution involves an efficient
multi-stage framework that restores a high-quality image from distorted frames.
Specifically, a frame selection algorithm based on sharpness is first utilized
to select the sharpest set of distorted frames. Next, each frame in the
selected frames is aligned to suppress geometric distortion through
optical-flow-based image registration. Then, a region-based image fusion method
with DT-CWT is utilized to mitigate the blur caused by the turbulence. Finally,
a learning-based deartifacts method is applied to remove the artifacts in the
fused image, generating a high-quality outuput. Our framework can handle both
hot-air text dataset and turbulence text dataset provided in the final testing
phase and achieved 1st place in text recognition accuracy. Our code will be
available at https://github.com/xsqhust/Turbulence_Removal.
