# Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution [[arXiv]](https://arxiv.org/pdf/2003.07018.pdf)
Pytorch implementation for "Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution".
<p align="center">
<img src="imgs/dual.png" alt="Dual Regression Scheme" width="90%" align=center />
</p>
## Dependencies
```
Python>=3.7, PyTorch>=1.1, numpy, skimage, imageio, matplotlib, tqdm
```
## Quickstart (Model Testing)
Results of our [pretrained models](https://github.com/guoyongcs/DRN/releases):
| 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](https://cv.snu.ac.kr/research/EDSR/benchmark.tar), including Set5, Set14, B100, Urban100, Manga109. Note that using an old PyTorch version (earlier than 1.1) would yield wrong results.
```bash
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
```
- DATA_DIR: path to save data
- SAVE_DIR: path to save experiment results
- DATA_TEST: the data to be tested, such as Set5, Set14, B100, Urban100, and Manga109
- SCALE: super resolution scale, such as 4 and 8
- MODEL: model type, such as DRN-S and DRN-L
- PRETRAINED_MODEL: path of the pretrained model
For example, you can use the following command to test our DRN-S model for 4x SR.
```bash
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
```
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
```
## Training Method
We use DF2K dataset (the combination of [DIV2K](https://data.vision.ee.ethz.ch/cvl/DIV2K/) and [Flickr2K](http://cv.snu.ac.kr/research/EDSR/Flickr2K.tar) datasets) to train DRN-S and DRN-L.
```bash
python main.py --data_dir $DATA_DIR$ \
--scale $SCALE$ --model $MODEL$ \
--save $SAVE_DIR$
```
- DATA_DIR: path to save data
- SCALE: super resolution scale, such as 4 and 8
- MODEL: model type, such as DRN-S and DRN-L
- SAVE_DIR: path to save experiment results
For example, you can use the following command to train the DRN-S model for 4x SR.
```bash
python main.py --data_dir ~/srdata \
--scale 4 --model DRN-S \
--save ../experiments
```
## Citation
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}
}
```
DRN-master_DRN_超分辨率重建_dualimage_源码
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