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LF-InterNet

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[ECCV 2020] Spatial-Angular Interaction for Light Field Image Super-Resolution

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# LF-InterNet

### PyTorch implementation of "Spatial-Angular Interaction for Light Field Image Super-Resolution", [*ECCV 2020*](https://arxiv.org/pdf/1912.07849.pdf).
 
 <a href="https://wyqdatabase.s3-us-west-1.amazonaws.com/LF-InterNet.mp4"><img src="https://raw.github.com/YingqianWang/LF-InterNet/master/Figs/demo-LF-InterNet.png" width="80%"></a>

#### News: We recommend our newly-released repository [*BasicLFSR*](https://github.com/ZhengyuLiang24/BasicLFSR) for the implementation of our LF-InterNet. [*BasicLFSR*](https://github.com/ZhengyuLiang24/BasicLFSR) is an open-source and easy-to-use toolbox for LF image SR. A number of milestone methods have been implemented (retrained) in a unified framework in [*BasicLFSR*](https://github.com/ZhengyuLiang24/BasicLFSR).

## Network Architecture:
 <img src="https://raw.github.com/YingqianWang/LF-InterNet/master/Figs/Network.jpg" width="80%">

## Codes and Models:

### Requirement:
* PyTorch 1.3.0, torchvision 0.4.1. The code is tested with python=3.7, cuda=9.0.
* Matlab (For training/test data generation and performance evaluation)

### Test:
* Download the test sets and unzip them to `./data`. Here, we provide a demo test set (<a href="https://wyqdatabase.s3-us-west-1.amazonaws.com/data_demo.zip">data_demo.zip</a>) which only includes one test scene, and we also provide the full test set on [Baidu Drive](https://pan.baidu.com/s/1LfopBUF_aKvfvUhSqA2Cew) (Key: NUDT) which is used in our paper.
* Download our pretrained models (<a href="https://wyqdatabase.s3-us-west-1.amazonaws.com/log.zip">log.zip</a>) and unzip them to `./log`.
* Run `GenerateDataForTest.m` to generate test data.
* Run `test.py` to perform a demo inference. Note that, the selected pretrained model should match the generated input data and the preset network architecture. Initial results (`.mat` files) will be saved to `./results`.
* Run `evaluation.m` to calculate PSNR and SSIM scores and transform initial results (`.mat` files) into `.png` images.

### Train:
Please switch to [*LF-InterNet_train*](https://github.com/YingqianWang/LF-InterNet/tree/LF-InterNet_train) for details.

## Results:

### Quantitative Results:
 <img src="https://raw.github.com/YingqianWang/LF-InterNet/master/Figs/Quantitative.jpg" width="70%">

### Visual Comparisons:
 <img src="https://raw.github.com/YingqianWang/LF-InterNet/master/Figs/Qualitative.jpg" width="100%">

### Efficiency:
 <img src="https://raw.github.com/YingqianWang/LF-InterNet/master/Figs/Efficiency.jpg" width="80%">

### Performance w.r.t. Perspectives:
 <img src="https://raw.github.com/YingqianWang/LF-InterNet/master/Figs/PwrtP.jpg" width="100%">

### Performance on Unseen Datasets:
 <img src="https://raw.github.com/YingqianWang/LF-InterNet/master/Figs/UnseenData.jpg" width="80%">

### Performance on Real LFs:
 <img src="https://raw.github.com/YingqianWang/LF-InterNet/master/Figs/VisualReal.jpg" width="100%">

## Citiation
**If you find this work helpful, please consider citing the following paper:**
```
@InProceedings{LF-InterNet,
  author    = {Wang, Yingqian and Wang, Longguang and Yang, Jungang and An, Wei and Yu, Jingyi and Guo, Yulan},
  title     = {Spatial-Angular Interaction for Light Field Image Super-Resolution},
  booktitle = {European Conference on Computer Vision (ECCV)},
  pages     = {290-308},
  year      = {2020},
}
```

## Contact
Any question regarding this work can be addressed to [wangyingqian16@nudt.edu.cn](wangyingqian16@nudt.edu.cn).

LF-InterNet

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