Learning-based image deraining methods have made great progress. However, the lack of large-scale high-quality paired training samples is the main bottleneck to hamper the real image deraining (RID). To address this dilemma and advance RID, we construct a Large-scale High-quality Paired real rain benchmark (LHP-Rain), including 3000 video sequences with 1 million high-resolution (1920*1080) frame pairs. The advantages of the proposed dataset over the existing ones are three-fold: rain with higher-diversity and larger-scale, image with higher-resolution and higher-quality ground-truth. Specifically, the real rains in LHP-Rain not only contain the classical rain streak/veiling/occlusion in the sky, but also the \textbf{splashing on the ground} overlooked by deraining community. Moreover, we propose a novel robust low-rank tensor recovery model to generate the GT with better separating the static background from the dynamic rain. In addition, we design a simple transformer-based single image deraining baseline, which simultaneously utilize the self-attention and cross-layer attention within the image and rain layer with discriminative feature representation. Extensive experiments verify the superiority of the proposed dataset and deraining method over state-of-the-art.

翻译：基于学习的图像去雨方法已取得显著进展。然而，缺乏大规模高质量配对训练样本是制约真实图像去雨（RID）发展的主要瓶颈。为解决这一困境并推动RID发展，我们构建了一个大规模高质量配对真实降雨基准（LHP-Rain），包含3000个视频序列及100万高分辨率（1920×1080）帧对。该数据集相较于现有数据集具有三大优势：雨滴呈现更高多样性与更大规模、图像具有更高分辨率与更优质量的真值。具体而言，LHP-Rain中的真实降雨不仅包含传统天空区域的雨线/雨幕/遮挡，还涵盖了去雨领域长期忽略的**地面溅射**现象。此外，我们提出一种新颖的鲁棒低秩张量恢复模型来生成真值，能更好分离静态背景与动态降雨。同时，我们设计了一个基于Transformer的简单单幅图像去雨基线，该网络同时利用自注意力机制与跨层注意力机制，在图像层与雨层中实现判别性特征表示。大量实验验证了所提数据集及去雨方法相较于现有最优方法的优越性。