In this technical report, we briefly introduce the solution of our team
HUST\li VIE for GT-Rain Challenge in CVPR 2023 UG$^{2}$+ Track 3. In this task,
we propose an efficient two-stage framework to reconstruct a clear image from
rainy frames. Firstly, a low-rank based video deraining method is utilized to
generate pseudo GT, which fully takes the advantage of multi and aligned rainy
frames. Secondly, a transformer-based single image deraining network Uformer is
implemented to pre-train on large real rain dataset and then fine-tuned on
pseudo GT to further improve image restoration. Moreover, in terms of visual
pleasing effect, a comprehensive image processor module is utilized at the end
of pipeline. Our overall framework is elaborately designed and able to handle
both heavy rainy and foggy sequences provided in the final testing phase.
Finally, we rank 1st on the average structural similarity (SSIM) and rank 2nd
on the average peak signal-to-noise ratio (PSNR). Our code is available at
this https URL

本文介绍了我们团队在 CVPR 2023 UG$^{2}$+ Track 3 GT-Rain Challenge 中的解决方案，提出了一种高效的两阶段框架，利用低秩视频去雨方法生成伪 GT，再加以改进，使用大规模真实雨数据集进行预训练并在伪 GT 上进行微调，最终采用全面的图像处理器模块，我们的整体框架设计精心并能够处理在测试阶段提供的重雨和雾气序列。在平均结构相似性 (SSIM) 上获得第一名，在平均峰值信噪比 (PSNR) 上获得第二名。