Hi-RSMamba: Hierarchical Mamba for Remote Sensing Image Restoration Under Adverse Weather

Remote sensing images are frequently affected by adverse weather conditions, such as haze and raindrops, which degrade image quality and subsequently impair the performance of downstream vision tasks. Currently, mainstream methods for remote sensing image restoration primarily rely on Convolutional Neural Networks (CNNs) and Transformer architectures. However, CNNs face limitations in handling long-range dependencies, while Transformers are constrained by computational efficiency, making it difficult to strike a balance between performance and efficiency. To address these issues, we propose a hierarchical state-space model for remote sensing image restoration, termed hierarchical RS Mamba (Hi-RSMamba). This model enhances contextual modeling by integrating multiscale representations through both global and local state-space models. Specifically, we introduce a hierarchical state-space model (HSSM) that improves the original state-space module by incorporating hierarchical feature representations while preserving local 2-D dependencies, thus enabling better aggregation of rich local and global information. Furthermore, given the complementary nature of global and local dependencies, we design a gated feedforward network to adaptively assist the broadcaste of these multiscale features, allowing for high-quality image background reconstruction. Extensive experimental results demonstrate that Hi-RSMamba exhibits significant advantages on widely used benchmark datasets in remote sensing image restoration tasks.