This repository contains the implementation of the following paper:
Robust Reference-based Super-Resolution via C2-Matching
Yuming Jiang, Kelvin C.K. Chan, Xintao Wang, Chen Change Loy, Ziwei Liu
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021
[Paper] [Project Page] [WR-SR Dataset]
- Python >= 3.7
- PyTorch >= 1.4
- CUDA 10.0 or CUDA 10.1
- GCC 5.4.0
-
Clone Repo
git clone [email protected]:yumingj/C2-Matching.git
-
Create Conda Environment
conda create --name c2_matching python=3.7 conda activate c2_matching
-
Install Dependencies
cd C2-Matching conda install pytorch=1.4.0 torchvision cudatoolkit=10.0 -c pytorch pip install mmcv==0.4.4 pip install -r requirements.txt
-
Install MMSR and DCNv2
python setup.py develop cd mmsr/models/archs/DCNv2 python setup.py build develop
- Train Set: CUFED Dataset
- Test Set: WR-SR Dataset, CUFED5 Dataset
Please refer to Datasets.md for pre-processing and more details.
Downloading the pretrained models from this link and put them under experiments/pretrained_models folder
.
We provide quick test code with the pretrained model.
-
Modify the paths to dataset and pretrained model in the following yaml files for configuration.
./options/test/test_C2_matching.yml ./options/test/test_C2_matching_mse.yml
-
Run test code for models trained using GAN loss.
python mmsr/test.py -opt "options/test/test_C2_matching.yml"
Check out the results in
./results
. -
Run test code for models trained using only reconstruction loss.
python mmsr/test.py -opt "options/test/test_C2_matching_mse.yml"
Check out the results in in
./results
All logging files in the training process, e.g., log message, checkpoints, and snapshots, will be saved to ./experiments
and ./tb_logger
directory.
-
Modify the paths to dataset in the following yaml files for configuration.
./options/train/stage1_teacher_contras_network.yml ./options/train/stage2_student_contras_network.yml ./options/train/stage3_restoration_gan.yml
-
Stage 1: Train teacher contrastive network.
python mmsr/train.py -opt "options/train/stage1_teacher_contras_network.yml"
-
Stage 2: Train student contrastive network.
# add the path to *pretrain_model_teacher* in the following yaml # the path to *pretrain_model_teacher* is the model obtained in stage1 ./options/train/stage2_student_contras_network.yml python mmsr/train.py -opt "options/train/stage2_student_contras_network.yml"
-
Stage 3: Train restoration network.
# add the path to *pretrain_model_feature_extractor* in the following yaml # the path to *pretrain_model_feature_extractor* is the model obtained in stage2 ./options/train/stage3_restoration_gan.yml python mmsr/train.py -opt "options/train/stage3_restoration_gan.yml" # if you wish to train the restoration network with only mse loss # prepare the dataset path and pretrained model path in the following yaml ./options/train/stage3_restoration_mse.yml python mmsr/train.py -opt "options/train/stage3_restoration_mse.yml"
For more results on the benchmarks, you can directly download our C2-Matching results from here.
Check out our Webly-Reference (WR-SR) SR Dataset through this link! We also provide the baseline results for a quick comparison in this link.
Webly-Reference SR dataset is a test dataset for evaluating Ref-SR methods. It has the following advantages:
- Collected in a more realistic way: Reference images are searched using Google Image.
- More diverse than previous datasets.
If you find our repo useful for your research, please consider citing our paper:
@InProceedings{jiang2021c2matching,
author = {Yuming Jiang and Kelvin C.K. Chan and Xintao Wang and Chen Change Loy and Ziwei Liu},
title = {Robust Reference-based Super-Resolution via C2-Matching},
booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2021}
}
This project is open sourced under MIT license. The code framework is mainly modified from BasicSR and MMSR (Now reorganized as MMEditing). Please refer to the original repo for more usage and documents.
If you have any question, please feel free to contact us via [email protected]
.