轻量化遥感图像超分辨率，用稀疏注意力提升速度与质量
HIMOSA: Efficient Remote Sensing Image Super-Resolution with Hierarchical Mixture of Sparse Attention
- 利用遥感图像冗余性，设计内容感知稀疏注意力机制
- 在多个数据集上实现顶尖性能且推理速度快
- 适合实时灾情监测等对效率要求高的遥感场景
在灾害检测与应急响应等遥感应用中，实时效率和模型轻量化至关重要。现有遥感图像超分辨率方法常在性能与计算效率间面临权衡。本文提出一种轻量级超分辨率框架HIMOSA，通过利用遥感图像的固有冗余性，引入内容感知稀疏注意力机制，实现快速推理的同时保持强重建能力。此外，为有效捕捉遥感图像中的多尺度重复模式，设计分层窗口扩展策略，并通过调整注意力稀疏度降低计算复杂度。在多个遥感数据集上的大量实验表明，该方法在保持计算高效的同时达到当前最优性能。
原文摘要 · Abstract (English)
In remote sensing applications, such as disaster detection and response, real-time efficiency and model lightweighting are of critical importance. Consequently, existing remote sensing image super-resolution methods often face a trade-off between model performance and computational efficiency. In this paper, we propose a lightweight super-resolution framework for remote sensing imagery, named HIMOSA. Specifically, HIMOSA leverages the inherent redundancy in remote sensing imagery and introduces a content-aware sparse attention mechanism, enabling the model to achieve fast inference while maintaining strong reconstruction performance. Furthermore, to effectively leverage the multi-scale repetitive patterns found in remote sensing imagery, we introduce a hierarchical window expansion and reduce the computational complexity by adjusting the sparsity of the attention. Extensive experiments on multiple remote sensing datasets demonstrate that our method achieves state-of-the-art performance while maintaining computational efficiency.
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