RSDehamba：面向遥感卫星图像去雾的轻量级视觉Mamba模型
RSDehamba: Lightweight Vision Mamba for Remote Sensing Satellite Image Dehazing
AI总结：
针对遥感图像去雾中现有方法难以兼顾长程依赖建模与计算效率的问题，提出基于mamba的轻量级网络RSDehamba，集成SSM到U-Net架构，通过VDB和DSM模块提升雾特征提取能力，性能超越现有SOTA方法。
摘要：
遥感图像去雾（RSID）旨在消除非均匀且物理形态不规则的雾霭因素，实现高质量的图像复原。卷积神经网络（CNNs）与Transformer的出现已在RSID领域取得了显著进展，但这类方法往往难以在充分的长程依赖建模与保持计算效率之间实现平衡。为此，我们在RSID领域提出了首个基于mamba架构的轻量级网络RSDehamba。受近期兴起的选择性状态空间模型（Selective State Space Model, SSM）在建模线性复杂度与长程依赖方面优异性能的启发，我们设计的RSDehamba将SSM框架集成到U-Net架构中。具体而言，我们提出视觉Dehamba模块（Vision Dehamba Block, VDB）作为整个网络的核心组件，利用SSM的线性复杂度实现全局上下文编码能力。同时，我们设计了方向感知扫描模块（Direction-aware Scan Module, DSM），动态聚合不同方向域的特征交互，有效提升对雾霭空间变化分布的感知灵活性。通过这种方式，我们的RSDehamba充分展现了在空间距离依赖捕获与通道信息交换方面的优势，能够更好地提取雾霭特征。在广泛使用的基准数据集上的大量实验结果表明，RSDehamba的性能优于现有最先进方法。
英文摘要：
Remote sensing image dehazing (RSID) aims to remove nonuniform and physically irregular haze factors for high-quality image restoration. The emergence of CNNs and Transformers has taken extraordinary strides in the RSID arena. However, these methods often struggle to demonstrate the balance of adequate long-range dependency modeling and maintaining computational efficiency. To this end, we propose the first lightweight network on the mamba-based model called RSDhamba in the field of RSID. Greatly inspired by the recent rise of Selective State Space Model (SSM) for its superior performance in modeling linear complexity and remote dependencies, our designed RSDehamba integrates the SSM framework into the U-Net architecture. Specifically, we propose the Vision Dehamba Block (VDB) as the core component of the overall network, which utilizes the linear complexity of SSM to achieve the capability of global context encoding. Simultaneously, the Direction-aware Scan Module (DSM) is designed to dynamically aggregate feature exchanges over different directional domains to effectively enhance the flexibility of sensing the spatially varying distribution of haze. In this way, our RSDhamba fully demonstrates the superiority of spatial distance capture dependencies and channel information exchange for better extraction of haze features. Extensive experimental results on widely used benchmarks validate the surpassing performance of our RSDehamba against existing state-of-the-art methods.
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。
