Awesome
PAMS: Quantized Super-Resolution via Parameterized Max Scale
This resposity is the official implementation of our ECCV2020 paper.
Our implementation is based on EDSR(PyTorch).
Dependent
- Python 3.6
- PyTorch == 1.1.0
- coloredlogs >= 14.0
- scikit-image
Datasets
Please download DIV2K datasets from here for training and benchmark datasets for testing. Then, organise the dataset directory as follows:
datasets
benchmark
DIV2K
Usage
- train
python main.py --scale 4 --k_bits 8 \
--pre_train ../pretrained/edsr_baseline_x4.pt \
--data_test Set14 --save edsr_x4/8bit/ \
--dir_data ./datasets --model EDSR
- test
python main.py --scale 4 --k_bits 8 \
--pre_train ../pretrained/edsr_x4 --save_results \
--data_test Set5+Set14+B100+Urban100 \
--save edsr_x4/8bit/ --dir_data ./datasets
--test_only --refine [REFINE] --model EDSR
set
--refine
to the saved model path for testing model.
More runnig scripts can be found in run.sh
.
- PSNR/SSIM
After saving the images, modify path inmetrics/calculate_PSNR_SSIM.m
to generate results.
matlab -nodesktop -nosplash -r "calculate_PSNR_SSIM('$dataset',$scale,$bit);quit"
refer to metrics/run.sh
for more details.
Trained Models
We also provide our baseline models below. Enjoy your training and testing! Google Drive.
Citations
If our paper helps your research, please cite it in your publications:
@article{li2020pams,
title={PAMS: Quantized Super-Resolution via Parameterized Max Scale},
author={Li, Huixia and Yan, Chenqian and Lin, Shaohui and Zheng, Xiawu and Li, Yuchao and Zhang, Baochang and Yang, Fan and Ji, Rongrong},
journal={arXiv preprint arXiv:2011.04212},
year={2020},
publisher={Springer}
}