Authors:
Christopher Mendoza-Dávila
;
David Porta-Montes
and
Willy Ugarte
Affiliation:
Department of Computer Science, Universidad Peruana de Ciencias Aplicadas, Lima, Peru
Keyword(s):
Photo Restoration, GAN, Image Inpainting, CNN, Image Classification, Deep Learning, Machine Learning Models.
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.