COZ

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Official dataset and code of Continuous Optical Zooming: A Benchmark for Arbitrary-Scale Image Super-Resolution in Real World[CVPR2024].

COZ 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

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## Continuous Optical Zooming Dataset

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

Supplementary training data for COZ V2 version (More data, more accurate data alignment) is now released: [COZv2_add - Google Drive](https://drive.google.com/file/d/1Fbg67YVdwOpv-T1cWqpAwtrw-b6d5iw4/view?usp=sharing)
<img width="1042" height="519" alt="image" src="https://github.com/user-attachments/assets/d0a2cfc0-956a-4653-a6ac-538035428e94" />

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

Official PyTorch implementation of LMI network.
<img width="1599" height="755" alt="image" src="https://github.com/user-attachments/assets/33082dac-4072-4d78-9408-e359e0f64f02" />

### 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}
> }
> ```

COZ

COZ 최신버젼 다운로드

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COZ

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