Proposal of a New Approach Using Deep Learning for QR Code Embedding

Kanaru Kumabuchi, Hiroyuki Kobayashi

2023

Abstract

The purpose of this research is to enhance the technique of embedding QR codes into arbitrary images using deep learning. Previous approaches faced the issue of compromising the quality when embedding QR codes into arbitrary images. We address this problem by proposing a deep learning model and learning method that can improve the quality of embedded images and accurately recover QR codes. Specifically, we design a new model using deep learning that embeds QR codes into images while minimizing the degradation of image quality. The effectiveness of the proposed model and learning method is validated through experiments, demonstrating the enhancement of image quality in the embedded images and accurate QR code recovery.

Paper Citation

in Harvard Style

Kumabuchi K. and Kobayashi H. (2023). Proposal of a New Approach Using Deep Learning for QR Code Embedding. In Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-758-670-5, SciTePress, pages 342-345. DOI: 10.5220/0012238900003543

in Bibtex Style

@conference{icinco23,
author={Kanaru Kumabuchi and Hiroyuki Kobayashi},
title={Proposal of a New Approach Using Deep Learning for QR Code Embedding},
booktitle={Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2023},
pages={342-345},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012238900003543},
isbn={978-989-758-670-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - Proposal of a New Approach Using Deep Learning for QR Code Embedding
SN - 978-989-758-670-5
AU - Kumabuchi K.
AU - Kobayashi H.
PY - 2023
SP - 342
EP - 345
DO - 10.5220/0012238900003543
PB - SciTePress