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.

翻译：遥感图像去雾旨在去除非均匀及物理不规则雾霾因素，实现高质量图像复原。卷积神经网络与Transformer的出现推动了该领域的突破性进展，但这类方法常难以兼顾充分的长距离依赖建模与计算效率。为此，我们提出了遥感图像去雾领域首个基于Mamba模型的轻量级网络RSDehamba。受近期选择性状态空间模型在线性复杂度建模与远程依赖处理方面卓越表现的启发，所设计的RSDehamba将SSM框架集成至U-Net架构中。具体而言，我们提出了视觉去雾块作为整体网络核心组件，利用SSM的线性复杂度实现全局上下文编码能力。同时，设计方向感知扫描模块，通过动态聚合不同方向域的特征交换，有效增强对雾霾空间非均匀分布的感知灵活性。通过这种方式，RSDehamba充分展现了空间距离捕获依赖与通道信息交换对雾霾特征提取的优越性。在广泛采用的基准数据集上的大量实验结果表明，本方法性能超越现有最优方法。