Abstract
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.
Abstract (translated)
在遥感应用中，如灾害检测和响应等领域，实时效率和模型轻量化至关重要。因此，现有的遥感图像超分辨率方法往往需要在模型性能与计算效率之间做出权衡。本文提出了一种用于遥感影像的轻量级超分辨率框架，命名为HIMOSA。具体而言，HIMOSA利用了遥感影像中存在的内在冗余，并引入了一种基于内容感知的稀疏注意力机制，使模型能够在保持强大重建性能的同时实现快速推理。此外，为了有效地利用遥感图像中发现的多尺度重复模式，我们提出了层次窗口扩展方法，并通过调整注意力的稀疏性来降低计算复杂度。在多个遥感数据集上的大量实验表明，我们的方法在保持计算效率的前提下实现了最先进的性能。
URL
https://arxiv.org/abs/2512.00275
