PyTorch implementation of "Spatial-Angular Interaction for Light Field Image Super-Resolution", ECCV 2020. [PDF] [Presentation]
- 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)
Please switch to LF-InterNet_train for details.
- Download the test sets and unzip them to
./data
. Here, we provide a demo test set (data_demo.zip) which only includes one test scene, and we also provide the full test set on Baidu Drive (Key: NUDT) which is used in our paper. - Download our pretrained models (log.zip) 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.
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},
}
Any question regarding this work can be addressed to [email protected].