从天空到地面：面向真实图像去雨的大规模基准与简单基线
From Sky to the Ground: A Large-scale Benchmark and Simple Baseline Towards Real Rain Removal
AI总结：
针对真实图像去雨缺乏大规模高质量成对数据的问题，本文构建LHP-Rain基准，涵盖地面溅雨，并提出低秩张量真值恢复模型及transformer去雨基线。
摘要：
基于学习的图像去雨方法已经取得了巨大进展。然而，缺乏大规模高质量成对训练样本是阻碍真实图像去雨（RID）的主要瓶颈。为解决这一困境并推进RID，我们构建了一个大规模高质量成对真实雨景基准（LHP-Rain），其中包含3000个视频序列，共计100万对高分辨率（1920*1080）帧图像。所提出数据集相对于现有数据集的优势体现在三个方面：雨景具有更高的多样性和更大的规模，图像具有更高的分辨率以及更高质量的真值。具体而言，LHP-Rain中的真实雨不仅包含天空中经典的雨纹、雨雾和遮挡，还包含去雨领域长期忽视的地面溅雨。此外，我们提出了一种新颖的鲁棒低秩张量恢复模型，用于生成真值，从而更好地将静态背景与动态雨分离。另外，我们设计了一个简单的基于transformer的单幅图像去雨基线，该基线同时利用图像层和雨层内部的自注意力与跨层注意力，以获得具有判别性的特征表示。大量实验验证了所提出数据集和去雨方法相对于最先进方法的优越性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
