TY - GEN
T1 - PhotoRestorer
T2 - 19th International Conference on Web Information Systems and Technologies, WEBIST 2023
AU - Mendoza-Dávila, Christopher
AU - Porta-Montes, David
AU - Ugarte, Willy
N1 - Publisher Copyright:
Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - CNN
KW - Deep Learning
KW - GAN
KW - Image Classification
KW - Image Inpainting
KW - Machine Learning Models
KW - Photo Restoration
UR - https://www.scopus.com/pages/publications/85179582689
U2 - 10.5220/0012190000003584
DO - 10.5220/0012190000003584
M3 - Contribución a la conferencia
AN - SCOPUS:85179582689
T3 - International Conference on Web Information Systems and Technologies, WEBIST - Proceedings
SP - 104
EP - 112
BT - Proceedings of the 19th International Conference on Web Information Systems and Technologies, WEBIST 2023
A2 - Penalvo, Francisco Garcia
A2 - Marchiori, Massimo
PB - Science and Technology Publications, Lda
Y2 - 15 November 2023 through 17 November 2023
ER -