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.

翻译：基于Transformer的单幅图像去雨（SID）方法已取得显著成功，这主要归功于其在捕获长距离交互方面的强大能力。然而，我们注意到当前方法同时处理受雨影响和未受影响的区域，忽略了这些区域之间的差异，导致雨条纹与背景部分混淆，无法获得有效的交互，最终造成去雨效果欠佳。为解决上述问题，我们提出了区域Transformer（Regformer），这是一种新颖的SID方法，强调独立处理受雨影响和未受影响的区域，同时考虑它们的综合影响以实现高质量图像重建。该方法的核心在于创新的区域Transformer块（RTB），它整合了区域掩码注意力（RMA）机制和混合门控前馈块（MGFB）。RTB用于对受雨影响和未受影响区域进行注意力选择，以及混合尺度的局部建模。RMA针对这两个区域及其交互生成注意力图，从而使模型能够捕获雨去除所需的全面的特征。为了更好地恢复高频纹理并捕获更多局部细节，我们开发了MGFB作为补偿模块以完成局部混合尺度建模。大量实验表明，我们的模型达到了最先进的性能，显著提升了图像去雨质量。我们的代码和训练模型已公开提供。