语义与内容至关重要：面向图像去雨的多先验分层Mamba
Semantics and Content Matter: Towards Multi-Prior Hierarchical Mamba for Image Deraining
AI总结：
针对现有图像去雨方法难以保留语义与空间细节的问题，本文提出多先验分层Mamba网络MPHM，融合CLIP与DINOv2先验并设计渐进融合模块与分层Mamba模块，在基准数据集和真实场景上取得更优性能。
摘要：
降雨会显著降低计算机视觉系统的性能，在自动驾驶和视频监控等应用场景中尤为明显。现有去雨方法虽已取得长足进展，但往往难以保障语义细节与空间细节的保真度。为解决这些局限，本文提出用于图像去雨的多先验分层Mamba（MPHM）网络。这一新颖架构协同整合了提供任务级语义引导的宏观语义文本先验（CLIP），以及提供场景感知结构信息的微观结构视觉先验（DINOv2）。为缓解异构先验间的潜在冲突，本文设计了渐进式先验融合注入（PFI）模块，可在不同解码器层级策略性地注入互补线索。同时，本文为骨干网络配备了精心设计的分层Mamba模块（HMM）以实现鲁棒的特征表示，该模块采用傅里叶增强双路径设计，可同时完成全局上下文建模与局部细节恢复。大量实验表明MPHM具备当前最优性能，在Rain200H数据集上取得了0.57dB的PSNR增益，同时在真实降雨场景下展现出更优的泛化能力。
英文摘要：
Rain significantly degrades the performance of computer vision systems, particularly in applications like autonomous driving and video surveillance. While existing deraining methods have made considerable progress, they often struggle with fidelity of semantic and spatial details. To address these limitations, we propose the Multi-Prior Hierarchical Mamba (MPHM) network for image deraining. This novel architecture synergistically integrates macro-semantic textual priors (CLIP) for task-level semantic guidance and micro-structural visual priors (DINOv2) for scene-aware structural information. To alleviate potential conflicts between heterogeneous priors, we devise a progressive Priors Fusion Injection (PFI) that strategically injects complementary cues at different decoder levels. Meanwhile, we equip the backbone network with an elaborate Hierarchical Mamba Module (HMM) to facilitate robust feature representation, featuring a Fourier-enhanced dual-path design that concurrently addresses global context modeling and local detail recovery. Comprehensive experiments demonstrate MPHM's state-of-the-art performance, achieving a 0.57 dB PSNR gain on the Rain200H dataset while delivering superior generalization on real-world rainy scenarios.
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。
