OPE-SR: Orthogonal Position Encoding for Designing a Parameter-free Upsampling Module in Arbitrary-scale Image Super-Resolution

This is the official implementation for the paper OPE-SR: "OPE-SR: Orthogonal Position Encoding for Designing a Parameter-free Upsampling Module in Arbitrary-scale Image Super-Resolution" (CVPR 2023)

Environment

python 3
pytorch 1.8.1+cu111
tensorboardX
timm, imageio, yaml, tqdm, matplotlib

Datasets

download the datasets. see LIIF.
put the datasets as follows:

datasets
├── benchmark
│   ├── B100
│   │   ├── HR
│   │   ├── LR_bicubic
│   ├── Set14
│   │   ├── HR
│   │   ├── LR_bicubic
│   ├── Set5
│   │   ├── HR
│   │   ├── LR_bicubic
│   └── Urban100
│       ├── HR
│       ├── LR_bicubic
├── div2k
│   ├── DIV2K_train_HR
│   ├── DIV2K_valid_HR
│   ├── DIV2K_valid_LR_bicubic_X2
│   ├── DIV2K_valid_LR_bicubic_X3
│   └── DIV2K_valid_LR_bicubic_X4

Checkpoints

download the pre-trained encoder:

Model	Download
EDSR-OPE	Google Drive
RDN-OPE	Google Drive

mkdir save, put the checkpoint as follows:

save
├── train_edsr-ope
│   ├── epoch-1000.pth
├── train_rdn-ope
│   ├── epoch-1000.pth

For Quick Demo

e.g. use EDSR encoder to test 0803.png in test_imgs folder for scale x4,x6,x8,x12, results will be saved in exp_folder.

python  ope_demo_zoom.py --exp_folder save/train_edsr-ope --ckpt_name epoch-1000.pth --hr_path test_imgs/0803.png --scale_list 4 6 8 12

For Reproducing Experiments

e.g. use EDSR encoder for div2k x12 scale.

python test_auto.py --exp_folder save/train_edsr-ope --test_config configs/test-configs/test_CIR-SR-div2k-x12.yaml

For Training

e.g. train EDSR encoder use GPU 0:

python train_ope_arbSR.py --config configs/train-div2k-configs/train_edsr-ope-1.yaml --tag exp_01 --gpu 0

e.g. train RDN encoder use GPU 0,1:

python train_ope_arbSR.py --config configs/train-div2k-configs/train_rdn-ope-1.yaml --tag exp_01 --gpu 0,1

Acknowledgements

We would like to express our gratitude to LIIF and LTE for their invaluable contribution to the development community. Our project is built upon them, which provided the foundation for much of our work. As beginners in this field, we learned a great deal from their project and were able to build upon it to create something new.

pxmapple/ope-sr