Our dataset is availble at COZ - Google Drive.
Official PyTorch implementation of LMI network.
Our code is based on Ubuntu 20.04, pytorch 1.11.0, CUDA 11.3 (NVIDIA RTX 3090 24GB, NVIDIA A40 48GB) and python 3.8.
Download our dataset and unzip it in the current directory.
Train: python train_real.py --config configs/train-real/lmi-edsr-baseline.yaml
Test: python test_real.py --config configs/test/test-RealAbrSR.yaml --model save/LMI-edsr-baseline/epoch-best.pth
Train: python train_real.py --config configs/train-real/lmi-rdn.yaml
Test: python test_real.py --config configs/test/test-RealAbrSR.yaml --model save/LMI-rdn/epoch-best.pth
We use NVIDIA RTX 3090 24GB for training, and NVIDIA A40 48GB for testing.
This code is built on LIIF and LTE. We thank the authors for sharing their codes.
If you find our work useful in your research, please consider citing our paper:
@InProceedings{Fu_2024_CVPR, author = {Fu, Huiyuan and Peng, Fei and Li, Xianwei and Li, Yejun and Wang, Xin and Ma, Huadong}, title = {Continuous Optical Zooming: A Benchmark for Arbitrary-Scale Image Super-Resolution in Real World}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024}, pages = {3035-3044} }