HIMOSA: Efficient Remote Sensing Image Super-Resolution with Hierarchical Mixture of Sparse Attention

TL;DR

HIMOSA is a lightweight remote sensing image super-resolution framework that uses hierarchical sparse attention to balance high reconstruction quality with fast, efficient inference, suitable for real-time applications.

Contribution

It introduces a content-aware sparse attention mechanism and hierarchical window expansion to improve efficiency and performance in remote sensing image super-resolution.

Findings

Achieves state-of-the-art super-resolution performance on remote sensing datasets.

Maintains high efficiency suitable for real-time applications.

Reduces computational complexity through hierarchical sparse attention.

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.