发表机构
School of Software Engineering, Jinling Institute of Technology; School of Information Engineering, Yancheng Institute of Technology（金陵科技学院软件工程学院; 盐城工学院信息工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对真实世界遥感图像去雾难题，提出基于MCAF-Net的双先验空间-频率网络DPSF-Net，融合暗通道先验与傅里叶分支，在RRSHID基准上达到最优性能。
AI 中文摘要
真实世界遥感图像去雾（RSID）仍然具有挑战性，因为大气散射、空间非均匀雾和颜色失真共同降低了结构和光谱信息。大多数深度学习方法依赖RGB输入和空间域特征提取，这限制了它们将全局背景雾与局部地表细节分离的能力。在此，我们提出了DPSF-Net，一种基于MCAF-Net构建的双先验空间-频率网络，用于真实世界RSID。该网络使用有雾RGB图像和暗通道先验（DCP）图作为联合输入，使物理退化线索能够引导端到端特征学习。空间-频率残差交互块将傅里叶单元分支引入多方向空间交互中，以建模大尺度雾分量。先验引导特征注意力模块自适应地融合先验和注意力特征，以减少颜色偏移和结构失真。选择性核互补融合模块通过双向残差互补门控和选择性核融合来筛选多尺度跳跃特征。大量实验表明，DPSF-Net在真实世界RRSHID遥感图像去雾基准上达到了最先进的性能，并在多个合成数据集上保持竞争力。此外，所提出的方法在恢复质量、参数数量和计算复杂度之间取得了良好的平衡，支持了双先验空间-频率建模的有效性。
英文摘要
Real-world remote sensing image dehazing (RSID) remains challenging because atmospheric scattering, spatially non-uniform haze and colour distortion jointly degrade structural and spectral information. Most deep learning methods rely on RGB inputs and spatial-domain feature extraction, which limits their ability to separate global background haze from local surface details. Here, we propose DPSF-Net, a dual-prior spatial-frequency network built on MCAF-Net for real-world RSID. The network uses hazy RGB images and dark channel prior (DCP) maps as joint inputs, allowing physical degradation cues to guide end-to-end feature learning. A spatial-frequency residual interaction block introduces a FourierUnit branch into multi-directional spatial interaction to model large-scale haze components. A prior-guided feature attention module adaptively fuses prior and attention features to reduce colour shift and structural distortion. A selective kernel complementary fusion module screens multi-scale skip features through bidirectional residual complementary gating and selective kernel fusion. Extensive experiments demonstrate that DPSF-Net achieves state-of-the-art performance on the real-world RRSHID remote sensing image dehazing benchmark and remains competitive across multiple synthetic datasets. Moreover, the proposed method strikes a favourable balance among restoration quality, parameter count and computational complexity, supporting the effectiveness of dual-prior spatial-frequency modelling.
