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In this technical report, we present the solution developed by our teamVIELab-HUST for text recognition through atmospheric turbulence in Track 2.1 ofthe CVPR 2023 UG$^{2}$+ challenge. Our solution involves an efficientmulti-stage framework that restores a high-quality image from distorted frames.Specifically, a frame selection algorithm based on sharpness is first utilizedto select the sharpest set of distorted frames. Next, each frame in theselected frames is aligned to suppress geometric distortion throughoptical-flow-based image registration. Then, a region-based image fusion methodwith 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 thefused image, generating a high-quality outuput. Our framework can handle bothhot-air text dataset and turbulence text dataset provided in the final testingphase and achieved 1st place in text recognition accuracy. Our code will beavailable at https://github.com/xsqhust/Turbulence_Removal.

1st Solution Places for CVPR 2023 UG$^{\textbf{2}}$+ Challenge Track 2.1-Text Recognition through Atmospheric Turbulence