Transformer-based Single Image Deraining (SID) methods have achieved
remarkable success, primarily attributed to their robust capability in
capturing long-range interactions. However, we've noticed that current methods
handle rain-affected and unaffected regions concurrently, overlooking the
disparities between these areas, resulting in confusion between rain streaks
and background parts, and inabilities to obtain effective interactions,
ultimately resulting in suboptimal deraining outcomes. To address the above
issue, we introduce the Region Transformer (Regformer), a novel SID method that
underlines the importance of independently processing rain-affected and
unaffected regions while considering their combined impact for high-quality
image reconstruction. The crux of our method is the innovative Region
Transformer Block (RTB), which integrates a Region Masked Attention (RMA)
mechanism and a Mixed Gate Forward Block (MGFB). Our RTB is used for attention
selection of rain-affected and unaffected regions and local modeling of mixed
scales. The RMA generates attention maps tailored to these two regions and
their interactions, enabling our model to capture comprehensive features
essential for rain removal. To better recover high-frequency textures and
capture more local details, we develop the MGFB as a compensation module to
complete local mixed scale modeling. Extensive experiments demonstrate that our
model reaches state-of-the-art performance, significantly improving the image
deraining quality. Our code and trained models are publicly available.

我们提出了一种名为 Regformer 的新型 SID 方法，强调在高质量图像重建中独立处理受雨影响和不受影响区域以及考虑它们的联合影响的重要性，通过创新的 Region Transformer Block (RTB) 来实现。实验结果表明，该模型达到了最先进的性能，显著提高了图像去雨质量。

深入探索区域信息：基于变换器的单幅图像去雨方法

Diving Deep into Regions: Exploiting Regional Information Transformer  for Single Image Deraining

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.

为了克服缺乏大规模高质量配对训练样本对真实图像除雨（RID）过程的阻碍，本研究构建了包含 3000 个视频序列和 100 万帧高分辨率（1920*1080）图像配对数据的大规模高质量真实雨数据集（LHP-Rain）。该数据集的优势有三个方面：具有更高多样性和更大规模的雨，高分辨率和高质量的合成图像。此外，研究提出了一种新颖的鲁棒低秩张量恢复模型用于生成更好地分离静态背景和动态雨的基准数据，并设计了基于 Transformer 的简单单幅图像除雨算法，通过自注意力和跨层注意力实现图像和雨层的辨别特征表示。大量实验证明了所提出数据集和除雨方法在最新研究中的优越性。