  In this technical report, we briefly introduce the solution of our team
VIELab-HUST for coded target restoration through atmospheric turbulence in CVPR
2023 UG$^2$+ Track 2.2. In this task, we propose an efficient multi-stage
framework to restore a high quality image from distorted frames. Specifically,
each distorted frame is initially aligned using image registration to suppress
geometric distortion. We subsequently select the sharpest set of registered
frames by employing a frame selection approach based on image sharpness, and
average them to produce an image that is largely free of geometric distortion,
albeit with blurriness. A learning-based deblurring method is then applied to
remove the residual blur in the averaged image. Finally, post-processing
techniques are utilized to further enhance the quality of the output image. Our
framework is capable of handling different kinds of coded target dataset
provided in the final testing phase, and ranked 1st on the final leaderboard.
Our code will be available at https://github.com/xsqhust/Turbulence_Removal.
