@inproceedings{63cf7100b3654a1ca0e26bee542ac47c,
title = "Epipolar plane images based light-field angular super-resolution network",
abstract = "Light field (LF) imaging has proven to be a promising technique in computer vision field. However, there is a tradeoff between spatial and angular resolution of LF images, which limits the application of LF cameras. Super-resolution (SR) of the angular domain is proposed to improve the angular resolution of LF images. However, most of the SR frameworks cannot adapt to LF datasets with multi-size disparities, especially the large disparities. In this paper, we proposed a learning-based SR framework named EASRnet. The EASRnet consists of three parts-Disparity adaptation, Feature extraction, and Feature restoration parts, and achieves angular SR tasks by using residual blocks and a structure with branches to reconstruct high-frequency details of up-sampled epipolar plane images (EPI). It employs an additional blur layer to accommodate LF datasets with different disparities. The experimental results show that the proposed approach can reconstruct novel view images with satisfactory accuracy.",
keywords = "Light field, Network, Super-resolution",
author = "Lijuan Su and Zimu Ye and Yuxiao Sui and Yan Yuan and Anqi Liu and Conghui Zhu",
note = "Publisher Copyright: {\textcopyright} COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.; 7th Asia Pacific Conference on Optics Manufacture, APCOM 2021 ; Conference date: 28-10-2021 Through 31-10-2021",
year = "2022",
doi = "10.1117/12.2615593",
language = "英语",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Jiubin Tan and Xiangang Luo and Ming Huang and Lingbao Kong and Dawei Zhang",
booktitle = "Seventh Asia Pacific Conference on Optics Manufacture, APCOM 2021",
address = "美国",
}