Multimodal Light-Field Camera with External Optical Filters Based on Unsupervised Learning

Takumi Shibata, Fumihiko Sakaue, Jun Sato

2023

Abstract

In this paper, we propose a method of capturing multimodal images in a single shot by attaching various optical filters to the front of a light-field (LF) camera. However, when a filter is attached to the front of the lens, the result of capturing images from each viewpoint will be a mixture of multiple modalities. Therefore, the proposed method uses a neural network that does not require prior learning to analyze such a modal mixture image to generate an image of all the modalities at all viewpoints. By using external filters as in the proposed method, it is possible to easily switch filters and realize a flexible configuration of the shooting system according to the purpose.

Paper Citation

in Harvard Style

Shibata T., Sakaue F. and Sato J. (2023). Multimodal Light-Field Camera with External Optical Filters Based on Unsupervised Learning. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 483-490. DOI: 10.5220/0011801900003417

in Bibtex Style

@conference{visapp23,
author={Takumi Shibata and Fumihiko Sakaue and Jun Sato},
title={Multimodal Light-Field Camera with External Optical Filters Based on Unsupervised Learning},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP},
year={2023},
pages={483-490},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011801900003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP
TI - Multimodal Light-Field Camera with External Optical Filters Based on Unsupervised Learning
SN - 978-989-758-634-7
AU - Shibata T.
AU - Sakaue F.
AU - Sato J.
PY - 2023
SP - 483
EP - 490
DO - 10.5220/0011801900003417
PB - SciTePress