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。

一种用于 GT-RAIN 挑战赛 CVPR 2023 Workshop UG$^{extbf {2}}$+ Track 3 的两阶段真实图像去雨方法

A Two-Stage Real Image Deraining Method for GT-RAIN Challenge CVPR 2023  Workshop UG$^{\textbf{2}}$+ Track 3

Rain is a common natural phenomenon. Taking images in the rain however often
results in degraded quality of images, thus compromises the performance of many
computer vision systems. Most existing de-rain algorithms use only one single
input image and aim to recover a clean image. Few work has exploited stereo
images. Moreover, even for single image based monocular deraining, many current
methods fail to complete the task satisfactorily because they mostly rely on
per pixel loss functions and ignore semantic information. In this paper, we
present a Paired Rain Removal Network (PRRNet), which exploits both stereo
images and semantic information. Specifically, we develop a Semantic-Aware
Deraining Module (SADM) which solves both tasks of semantic segmentation and
deraining of scenes, and a Semantic-Fusion Network (SFNet) and a View-Fusion
Network (VFNet) which fuse semantic information and multi-view information
respectively. In addition, we also introduce an Enhanced Paired Rain Removal
Network (EPRRNet) which exploits semantic prior to remove rain streaks from
stereo images. We first use a coarse deraining network to reduce the rain
streaks on the input images, and then adopt a pre-trained semantic segmentation
network to extract semantic features from the coarse derained image. Finally, a
parallel stereo deraining network fuses semantic and multi-view information to
restore finer results. We also propose new stereo based rainy datasets for
benchmarking. Experiments on both monocular and the newly proposed stereo rainy
datasets demonstrate that the proposed method achieves the state-of-the-art
performance.

本文提出了一种 Paired Rain Removal 网络（PRRNet），它在去除降雨图像上利用了语义信息和立体影像，并展示了其在单 ocular 和构建的立体降雨数据集上优于其他算法的性能。