文件列表:

LICENSE (1064, 2020-05-07)
checkpoint.py (3061, 2020-05-07)
data (0, 2020-05-07)
data\__init__.py (1160, 2020-05-07)
data\benchmark.py (509, 2020-05-07)
data\common.py (1931, 2020-05-07)
data\df2k.py (462, 2020-05-07)
data\srdata.py (3604, 2020-05-07)
imgs (0, 2020-05-07)
imgs\dual.png (445943, 2020-05-07)
loss.py (2662, 2020-05-07)
main.py (500, 2020-05-07)
model (0, 2020-05-07)
model\__init__.py (4254, 2020-05-07)
model\common.py (4267, 2020-05-07)
model\drn.py (3317, 2020-05-07)
option.py (4455, 2020-05-07)
trainer.py (5754, 2020-05-07)
utility.py (3606, 2020-05-07)

# 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".

## 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} } ```

近期下载者：

相关文件：

评论：[我要评论] [举报此文件]

收藏者：