PhotoRestorer: Restoration of Old or Damaged Portraits with Deep Learning

Christopher Mendoza-Dávila, David Porta-Montes, Willy Ugarte

2023

Abstract

Several studies have proposed different image restoration techniques, however most of them focus on restoring a single type of damage or, if they restore different types of damage, their results are not very good or have a long execution time, since they have a large margin for improvement. Therefore, we propose the creation of a convolutional neural network (CNN) to classify the type of damage of an image and, accordingly, use pretrained models to restore that type of damage. For the classifier we use the transfer learning technique using the Inception V3 model as the basis of our architecture. To train our classifier, we used the FFHQ dataset, which is a dataset of people’s faces, and using masks and functions, added different types of damage to the images. The results show that using a classifier to identify the type of damage in images is a good pre-restore option to reduce execution times and improve restored image results.

Paper Citation

in Harvard Style

Mendoza-Dávila C., Porta-Montes D. and Ugarte W. (2023). PhotoRestorer: Restoration of Old or Damaged Portraits with Deep Learning. In Proceedings of the 19th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST; ISBN 978-989-758-672-9, SciTePress, pages 104-112. DOI: 10.5220/0012190000003584

in Bibtex Style

@conference{webist23,
author={Christopher Mendoza-Dávila and David Porta-Montes and Willy Ugarte},
title={PhotoRestorer: Restoration of Old or Damaged Portraits with Deep Learning},
booktitle={Proceedings of the 19th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST},
year={2023},
pages={104-112},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012190000003584},
isbn={978-989-758-672-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST
TI - PhotoRestorer: Restoration of Old or Damaged Portraits with Deep Learning
SN - 978-989-758-672-9
AU - Mendoza-Dávila C.
AU - Porta-Montes D.
AU - Ugarte W.
PY - 2023
SP - 104
EP - 112
DO - 10.5220/0012190000003584
PB - SciTePress