Re-Learning ShiftIR for Super-Resolution of Carbon Nanotube Images

Yoshiki Kakamu, Takahiro Maruyama, Kazuhiro Hotta

2023

Abstract

In this study, we perform super-resolution of carbon nanotube images using Deep Learning. In order to achieve super-resolution with higher accuracy than conventional SwinIR, we introduce an encoder-decoder structure to input an image of larger size and a Shift mechanism for local feature extraction. In addition, we propose super-resolution method by re-training to perform super-resolution with high accuracy even with a small number of images. Experiments were conducted on DIV2K, General100, Set5, and carbon nanotube image dataset for evaluation. Experimental results confirmed that the proposed method provides higher accuracy than the conventional SwinIR, and showed that the proposed method can super-resolve carbon nanotube images. The main contribution is the proposal of a network model with better performance for super-resolution of carbon nanotube images even if there is no crisp supervised images. The proposed method is suitable for such images. Effectiveness of our method was demonstrated by experimental results on a general super-resolution dataset and a carbon nanotube image dataset.

Paper Citation

in Harvard Style

Kakamu Y., Maruyama T. and Hotta K. (2023). Re-Learning ShiftIR for Super-Resolution of Carbon Nanotube Images. 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 371-377. DOI: 10.5220/0011708200003417

in Bibtex Style

@conference{visapp23,
author={Yoshiki Kakamu and Takahiro Maruyama and Kazuhiro Hotta},
title={Re-Learning ShiftIR for Super-Resolution of Carbon Nanotube Images},
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={371-377},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011708200003417},
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 - Re-Learning ShiftIR for Super-Resolution of Carbon Nanotube Images
SN - 978-989-758-634-7
AU - Kakamu Y.
AU - Maruyama T.
AU - Hotta K.
PY - 2023
SP - 371
EP - 377
DO - 10.5220/0011708200003417
PB - SciTePress