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.

翻译：本技术报告介绍了团队VIELab-Hust在CVPR 2023 UG$^{2}$+挑战赛赛道2.1中针对大气湍流下文本识别问题所提出的解决方案。该方案采用高效的多阶段框架，从畸变帧中恢复高质量图像。具体而言：首先基于清晰度的帧选择算法筛选出最清晰的畸变帧集合；其次通过基于光流的图像配准抑制几何畸变；随后利用基于区域融合的DT-CWT方法减弱湍流导致的模糊效应；最终采用基于学习的去伪影方法消除融合图像中的伪影，生成高质量输出。该框架可同时处理最终测试阶段提供的热空气文本数据集与湍流文本数据集，在文本识别准确率方面荣获第一名。相关代码开源地址为：https://github.com/xsqhust/Turbulence_Removal。