Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution
Pytorch implementation for “Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution”.
Python>=3.7, PyTorch>=1.1, numpy, skimage, imageio, matplotlib, tqdm
Results of our pretrained models:
Model | Scale | #Params (M) | PSNR on Set5 (dB) |
---|---|---|---|
DRN-S | 4 | 4.8 | 32.68 |
8 | 5.4 | 27.41 | |
DRN-L | 4 | 9.8 | 32.74 |
8 | 10.0 | 27.43 |
You can evaluate our models on several widely used benchmark datasets, including Set5, Set14, B100, Urban100, Manga109. Note that using an old PyTorch version (earlier than 1.1) would yield wrong results.
Please organize the benchmark datasets using the following hierarchy.
- srdata
- benchmark
- Set5
- LR_bicubic
- X4
- babyx4.png
You can use the following script to obtain the testing results:
python main.py --data_dir $DATA_DIR$ \
--save $SAVE_DIR$ --data_test $DATA_TEST$ \
--scale $SCALE$ --model $MODEL$ \
--pre_train $PRETRAINED_MODEL$ \
--test_only --save_results
For example, you can use the following command to test our DRN-S model for 4x SR.
python main.py --data_dir ~/srdata \
--save ../experiments --data_test Set5 \
--scale 4 --model DRN-S \
--pre_train ../pretrained_models/DRNS4x.pt \
--test_only --save_results
You will obtain the output like this.
If you want to load the pretrained dual model, you can add the following option into the command.
--pre_train_dual ../pretrained_models/DRNS4x_dual_model.pt
We use DF2K dataset (the combination of DIV2K and Flickr2K datasets) to train DRN-S and DRN-L.
python main.py --data_dir $DATA_DIR$ \
--scale $SCALE$ --model $MODEL$ \
--save $SAVE_DIR$
For example, you can use the following command to train the DRN-S model for 4x SR.
python main.py --data_dir ~/srdata \
--scale 4 --model DRN-S \
--save ../experiments
If you use any part of this code in your research, please cite our paper:
@inproceedings{guo2020closed,
title={Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution},
author={Guo, Yong and Chen, Jian and Wang, Jingdong and Chen, Qi and Cao, Jiezhang and Deng, Zeshuai and Xu, Yanwu and Tan, Mingkui},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
year={2020}
}