PyTorch codes for "Frequency-Assisted Mamba for Remote Sensing Image Super-Resolution", IEEE Transactions on Multimedia (TMM), under review.
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.
git clone https://github.com/XY-boy/FreMamba.git
Please download the following remote sensing benchmarks: | Data Type | AID | DOTA-v1.0 | DIOR | NWPU-RESISC45 |
---|---|---|---|---|---|
Training | Download | None | None | None | |
Testing | Download | Download | Download | Download |
- CUDA 11.1
- Python 3.9.13
- PyTorch 1.9.1
- Torchvision 0.10.1
- causal_conv1d==1.0.0
- mamba_ssm==1.0.1
/GT/
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/···.png
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/LR/
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--data_dir
to your data path.--pretrained_sr
to your pre-trained model path. python eval_4x.py
python train_4x.py
Our work is built upon MambaIR. Thanks to the author for sharing this awesome work!
If you find our work helpful in your research, please consider citing it!
@ARTICLE{xiao2024fmsr,
author={Xiao, Yi and Yuan, Qiangqiang and Jiang, Kui and Chen, Yuzeng and Zhang, Qiang and Lin, Chia-Wen},
journal={IEEE Transactions on Multimedia},
title={Frequency-Assisted Mamba for Remote Sensing Image Super-Resolution},
year={2024},
volume={},
number={},
pages={1-14},
}