PRISM通过三阶段设计，高效去除图像雨痕并保留细节。
PRISM: Progressive Rain removal with Integrated State-space Modeling
- 分三阶段逐步去雨：粗提取、频域融合、精细修复
- 结合通道注意力与窗口空间变换，提升多尺度特征聚合
- 适合需要高精度图像清晰度的自动驾驶等场景
图像去雨是增强视觉任务清晰度的关键技术，尤其在自动驾驶中至关重要。现有单尺度模型难以同时实现细粒度恢复与全局一致性。为此，我们提出渐进式三阶段框架PRISM：Coarse Extraction Network（CENet）、Frequency Fusion Network（SFNet）和Refine Network（RNet）。CENet与SFNet采用新型混合注意力UNet（HA-UNet），通过融合通道注意力与窗口化空间变换器实现多尺度特征聚合；SFNet进一步引入混合域Mamba（HDMamba），联合建模空间语义与小波域特征。最后，RNet通过原分辨率子网络恢复细粒度结构。模型在学习高频雨痕特征的同时保持结构细节与全局上下文，显著提升图像质量。在多个数据集上，本方法性能优于近期主流去雨模型。
原文摘要 · Abstract (English)
Image deraining is an essential vision technique that removes rain streaks and water droplets, enhancing clarity for critical vision tasks like autonomous driving. However, current single-scale models struggle with fine-grained recovery and global consistency. To address this challenge, we propose Progressive Rain removal with Integrated State-space Modeling (PRISM), a progressive three-stage framework: Coarse Extraction Network (CENet), Frequency Fusion Network (SFNet), and Refine Network (RNet). Specifically, CENet and SFNet utilize a novel Hybrid Attention UNet (HA-UNet) for multi-scale feature aggregation by combining channel attention with windowed spatial transformers. Moreover, we propose Hybrid Domain Mamba (HDMamba) for SFNet to jointly model spatial semantics and wavelet domain characteristics. Finally, RNet recovers the fine-grained structures via an original-resolution subnetwork. Our model learns high-frequency rain characteristics while preserving structural details and maintaining global context, leading to improved image quality. Our method achieves competitive results on multiple datasets against recent deraining methods.
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