This repository is for 360SISR introduced in the following paper
Akito Nishiyama, Satoshi Ikehata, Kiyoharu Aizawa, "360° SINGLE IMAGE SUPER RESOLUTION VIA DISTORTION-AWARE NETWORK AND DISTORTED PERSPECTIVE IMAGES", ICIP 2021, [Paper]
The code is built on RCAN (PyTorch)
Effective 360° imaging requires a very high resolution because the field of view is extraordinarily high. Single-image super-resolution (SISR) applied to 360° imaging has the potential to solve the resolution/quality problem in this modality. In this paper, we exploit existing perspective SISR networks to address this problem by (1) introducing a distortion map as an additional input with the 360° distortion-aware loss function, and (2) augmenting the training 360° images by distorting the perspective images. We also present a new 360° image dataset from YouTube for training. Our extensive experiments show that how each component contributes to the better transfer from the perspective domain to the 360° domain and merging all the ideas leads to the best performance in quantitative and qualitative ways for the 360° SISR task.
This dataset is for research purposes only and is not for commercial use.
If you need the YouTube360 dataset, please contact us. We will share the download link.
If you find the dataset helpful in your resarch or work, please cite the following paper.
@INPROCEEDINGS{9506233,
author={Nishiyama, Akito and Ikehata, Satoshi and Aizawa, Kiyoharu},
booktitle={2021 IEEE International Conference on Image Processing (ICIP)},
title={360° Single Image Super Resolution via Distortion-Aware Network and Distorted Perspective Images},
year={2021},
volume={},
number={},
pages={1829-1833},
doi={10.1109/ICIP42928.2021.9506233}}
This code is built on RCAN (PyTorch) and EDSR (PyTorch). We thank the authors for sharing their codes of RCAN and EDSR.
If you have any question, please feel free to send an e-mail to aki.nishi.work@gmail.com.