In this technical report, we briefly introduce the solution of our team ''summer'' for Atomospheric Turbulence Mitigation in UG$^2$+ Challenge in CVPR 2022. In this task, we propose a unified end-to-end framework to reconstruct a high quality image from distorted frames, which is mainly consists of a Restormer-based image reconstruction module and a NIMA-based image quality assessment module. Our framework is efficient and generic, which is adapted to both hot-air image and text pattern. Moreover, we elaborately synthesize more than 10 thousands of images to simulate atmospheric turbulence. And these images improve the robustness of the model. Finally, we achieve the average accuracy of 98.53\% on the reconstruction result of the text patterns, ranking 1st on the final leaderboard.
翻译：在本技术报告中,我们简要地介绍了我们的“夏季”小组在2022年CVPR中以UG$2$+ CVPR中为原子层扰动缓减工作“夏季”的解决方案。在这个任务中,我们提出了一个统一端对端框架,从扭曲的框框中重建高质量的图像,主要包括以风暴为基础的图像重建模块和以NIMA为基础的图像质量评估模块。我们的框架既有效又通用,既适合热空气图像,又适合文本模式。此外,我们精心合成了10 000多张图像,以模拟大气动荡。这些图像提高了模型的稳健性。最后,我们实现了文本模式重建结果的平均准确度98.53 ⁇,在最后的首版上排第1位。
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