296-continuous-optical-zooming-a-benchmark-for-arbitrary-scale-image-super-resolution-in-real-worl

https://github.com/szu-advtech-2024/296-continuous-optical-zooming-a-benchmark-for-arbitrary-scale-image-super-resolution-in-real-worl

Scientific Fields

Repository

https://github.com/SZU-AdvTech-2024/296-Continuous-Optical-Zooming-A-Benchmark-for-Arbitrary-Scale-Image-Super-Resolution-in-Real-Worl/blob/main/

## Continuous Optical Zooming Dataset

Our dataset is availble at [COZ - Google Drive](https://drive.google.com/drive/folders/196vQw7Y6aLnoEsRYDV3MOETL7YU9HrDQ).

## Local Mix Implicit Network for Arbitrary-Scale Image Super-Resolution (LMI)

Official PyTorch implementation of LMI network.

### Installation

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.

### Data Preparation

Download our dataset and unzip it in the current directory.

### Train & Test

##### **EDSR-Baseline-LMI**

**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`

##### **RDN-LMI**

**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.

### Pretrained Checkpoints

**[EDSR-Baseline-LMI](https://drive.google.com/file/d/1-18tDJduD3sqYVOBPhu19YnJaHDkOiGr/view?usp=drive_link)**

**[RDN-LMI](https://drive.google.com/file/d/1-1iASRRn604jwgzxOy7NHdjX_EQ7L3L-/view?usp=drive_link)**

### Acknowledgements

This code is built on [LIIF](https://github.com/yinboc/liif) and [LTE](https://github.com/jaewon-lee-b/lte).  We thank the authors for sharing their codes.

## Citation

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}
> }
> ```

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