提出双域多尺度网络，提升单张图像去雨效果。
Learning Dual-Domain Multi-Scale Representations for Single Image Deraining
- 并行建模外部与内部多尺度特征，融合空间与频率域信息。
- 在六个基准数据集上达到当前最优性能。
- 适合需要高精度去雨的图像恢复场景。
现有图像去雨方法通常采用单输入单输出单尺度架构，忽略了外部与内部特征间的联合多尺度信息。同时，单一域表示过于受限，难以应对真实雨景的复杂性。为此，我们提出一种新的双域多尺度表征网络（DMSR）。核心思想是并行挖掘外部与内部域的联合多尺度表征，并结合空间与频率域优势以捕捉更全面的特征。具体包含两个关键组件：多尺度渐进空间精炼模块（MPSRM）和频域尺度混合器（FDSM）。MPSRM通过分层调制与融合策略，在内部域内实现多尺度专家信息的交互与耦合；FDSM在空间域提取多尺度局部信息，同时在频域建模全局依赖关系。大量实验表明，该模型在六个基准数据集上均达到当前最优性能。
原文摘要 · Abstract (English)
Existing image deraining methods typically rely on single-input, single-output, and single-scale architectures, which overlook the joint multi-scale information between external and internal features. Furthermore, single-domain representations are often too restrictive, limiting their ability to handle the complexities of real-world rain scenarios. To address these challenges, we propose a novel Dual-Domain Multi-Scale Representation Network (DMSR). The key idea is to exploit joint multi-scale representations from both external and internal domains in parallel while leveraging the strengths of both spatial and frequency domains to capture more comprehensive properties. Specifically, our method consists of two main components: the Multi-Scale Progressive Spatial Refinement Module (MPSRM) and the Frequency Domain Scale Mixer (FDSM). The MPSRM enables the interaction and coupling of multi-scale expert information within the internal domain using a hierarchical modulation and fusion strategy. The FDSM extracts multi-scale local information in the spatial domain, while also modeling global dependencies in the frequency domain. Extensive experiments show that our model achieves state-of-the-art performance across six benchmark datasets.
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