1st Solution Places for CVPR 2023 UG$^2$+ Challenge Track 2.2-Coded Target Restoration through Atmospheric Turbulence

• URL: http://arxiv.org/abs/2306.09379v1
• Date: Thu, 15 Jun 2023 09:06:48 GMT
• Title: 1st Solution Places for CVPR 2023 UG$^2$+ Challenge Track 2.2-Coded Target Restoration through Atmospheric Turbulence
• Authors: Shengqi Xu, Shuning Cao, Haoyue Liu, Xueyao Xiao, Yi Chang, Luxin Yan
• Abstract summary: This report introduces the solution of our team VIELab-HUST for coded target restoration through atmospheric turbulence in CVPR 2023 UG$2$+ Track 2.2. We propose an efficient multi-stage framework to restore a high quality image from distorted frames. 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.
• Score: 12.484269899245515
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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