Thesis Details
Comic Images Super-Resolution Using Deep Learning
This paper demonstrates a super-resolution method for improving the resolution and quality of comic images by using deep learning. The challenging part of the task was to keep the quality of the text parts and drawings simultaneously, without significant deformation of any part. Two deep neural networks were used to achieve satisfying results. U-Net network and its modification called Robust U-Net. The chosen loss functions to train these networks were the Mean Squared Error and Perceptual loss. The work contains experiments on U-Net and modified RUNet networks with a combination of each loss function. Additional experiments looked at how the number of used blocks from the VGG16 loss network affects the Perceptual loss function. Experiments have shown that a Robust U-Net network using a Perceptual loss with three extracted blocks got the best results.
single image super-resoltuion, deep learning, convolutional neural networks, comic images, U-Net, RUNet
Burget Lukáš, doc. Ing., Ph.D. (DCGM FIT BUT), člen
Holík Lukáš, doc. Mgr., Ph.D. (DITS FIT BUT), člen
Martínek Tomáš, doc. Ing., Ph.D. (DCSY FIT BUT), člen
Matoušek Petr, doc. Ing., Ph.D., M.A. (DIFS FIT BUT), člen
@bachelorsthesis{FITBT24494,
author = "Peter Zdraveck\'{y}",
type = "Bachelor's thesis",
title = "Comic Images Super-Resolution Using Deep Learning",
school = "Brno University of Technology, Faculty of Information Technology",
year = 2022,
location = "Brno, CZ",
language = "english",
url = "https://www.fit.vut.cz/study/thesis/24494/"
}