This repository contains the official implementation for SRNO introduced in the following paper:
Super-Resolution Neural Operator (CVPR 2023)
Our code is based on Ubuntu 18.04, pytorch 1.10.2, CUDA 11.3 and python 3.9.
python train.py --config configs/train_edsr-sronet.yaml
if you want to change encoder, please modify the yaml file
model:
name: sronet
args:
encoder_spec:
name: edsr-baseline ## or rdn
args:
no_upsampling: true
width: 256
blocks: 16Download a DIV2K pre-trained model.
| Model | Download |
|---|---|
| EDSR-baseline-SRNO | Google Drive |
| RDN-SRNO | Google Drive |
python test.py --config configs/test_srno.yaml --model edsr-baseline_epoch-1000.pth --mcell True
python demo.py --input input.png --model save/edsr-baseline_epoch-1000.pth --scale 2 --output output.png
If you find our work useful in your research, please consider citing:
@InProceedings{Wei_2023_CVPR,
author = {Wei, Min and Zhang, Xuesong},
title = {Super-Resolution Neural Operator},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2023},
pages = {18247-18256}
}