Recent progress in remote sensing image (RSI) super-resolution (SR) has
exhibited remarkable performance using deep neural networks, e.g.,
Convolutional Neural Networks and Transformers. However, existing SR methods
often suffer from either a limited receptive field or quadratic computational
overhead, resulting in sub-optimal global representation and unacceptable
computational costs in large-scale RSI. To alleviate these issues, we develop
the first attempt to integrate the Vision State Space Model (Mamba) for RSI-SR,
which specializes in processing large-scale RSI by capturing long-range
dependency with linear complexity. To achieve better SR reconstruction,
building upon Mamba, we devise a Frequency-assisted Mamba framework, dubbed
FMSR, to explore the spatial and frequent correlations. In particular, our FMSR
features a multi-level fusion architecture equipped with the Frequency
Selection Module (FSM), Vision State Space Module (VSSM), and Hybrid Gate
Module (HGM) to grasp their merits for effective spatial-frequency fusion.
Recognizing that global and local dependencies are complementary and both
beneficial for SR, we further recalibrate these multi-level features for
accurate feature fusion via learnable scaling adaptors. Extensive experiments
on AID, DOTA, and DIOR benchmarks demonstrate that our FMSR outperforms
state-of-the-art Transformer-based methods HAT-L in terms of PSNR by 0.11 dB on
average, while consuming only 28.05% and 19.08% of its memory consumption and
complexity, respectively.

提出了一种用于处理大规模远程感知图像超分辨率的频率辅助 Mamba 框架，通过多级融合结构和可学习的缩放适配器实现了准确的特征融合，相比于现有方法具有更高的峰值信噪比和更低的内存消耗和计算复杂度。