EPIT

This is the official implementation of "Learning Non-Local Spatial-Angular Correlation for Light Field Image Super-Resolution", ICCV 2023.

[paper] [arXiv] [project]

Training & Evaluation

Download the EPFL, HCInew, HCIold, INRIA and STFgantry datasets via Baidu Drive (key:7nzy) or OneDrive, and place the 5 datasets to the folder ./datasets/.
Run Generate_Data_for_SSR_Training.py to generate training data, and begin to train the EPIT (on 5x5 by default) for 2x/4x SR:
```
$ python train.py --scale_factor $2/4$
```
Run Generate_Data_for_SSR_Test.py to generate evaluation data, and you can quick run test.py to perform network inference by using our released models.
```
python test.py
```

Quantitative Results

Visual Comparison

Citiation

If you find this work helpful, please consider citing:

@InProceedings{Liang_2023_ICCV,
    author    = {Liang, Zhengyu and Wang, Yingqian and Wang, Longguang and Yang, Jungang and Zhou, Shilin and Guo, Yulan},
    title     = {Learning Non-Local Spatial-Angular Correlation for Light Field Image Super-Resolution},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2023},
    pages     = {12376-12386}
}

Related Projects

Contact

Welcome to raise issues or email to zyliang@nudt.edu.cn for any questions regarding our EPIT.

ZhengyuLiang24 / EPIT

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