Gated Multiple Feedback Network for Image Super-Resolution
This repository contains the PyTorch implementation for the proposed GMFN [arXiv].
The framework of our proposed GMFN. The colored arrows among different time steps denote the multiple feedback connections. The high-level information carried by them helps low-level features become more representative.
Demo
Clone SRFBN as the backbone and satisfy its requirements.
Test
-
Copy
./networks/gmfn_arch.pyintoSRFBN_CVPR19/networks/ -
Download the pre-trained models from Google driver or Baidu Netdisk, unzip and place them into
SRFBN_CVPR19/models. -
Copy
./options/test/toSRFBN_CVPR19/options/test/. -
Run commands
cd SRFBN_CVPR19and one of followings for evaluation on Set5:
python test.py -opt options/test/test_GMFN_x2.json
python test.py -opt options/test/test_GMFN_x3.json
python test.py -opt options/test/test_GMFN_x4.json
- Finally, PSNR/SSIM values for Set5 are shown on your screen, you can find the reconstruction images in
./results.
To test GMFN on other standard SR benchmarks or your own images, please refer to the instruction in SRFBN.
Train
- Prepare the training set according to this (1-3).
- Modify
./options/train/train_GMFN.jsonby following the instructions in./options/train/README.md. - Run commands:
cd SRFBN_CVPR19
python train.py -opt options/train/train_GNFN.json
-
You can monitor the training process in
./experiments. -
Finally, you can follow the test pipeline to evaluate the model trained by yourself.
Performance
Quantitative Results
Quantitative evaluation under scale factors x2, x3 and x4. The best performance is shown in bold and the second best performance is underlined.
More Qualitative Results (x4)
Acknowledgment
If you find our work useful in your research or publications, please consider citing:
@inproceedings{li2019gmfn,
author = {Li, Qilei and Li, Zhen and Lu, Lu and Jeon, Gwanggil and Liu, Kai and Yang, Xiaomin},
title = {Gated Multiple Feedback Network for Image Super-Resolution},
booktitle = {The British Machine Vision Conference (BMVC)},
year = {2019}
}
@inproceedings{li2019srfbn,
author = {Li, Zhen and Yang, Jinglei and Liu, Zheng and Yang, Xiaomin and Jeon, Gwanggil and Wu, Wei},
title = {Feedback Network for Image Super-Resolution},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year= {2019}
}
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