RT info:eu-repo/semantics/bachelorThesis
T1 Deep Learning en videojuegos: superresolución
A1 Abreu Díaz, Imar
A2 Grado En Ingeniería Informática
K1 Videojuegos
K1 Superresolución
K1 Inteligencia Artificial (AI)
K1 Redes Neuronales
K1 Redes Neuronales Convolucionales (CNN)
K1 Deep Learning
K1 Python
K1 TensorFlow
K1 Keras
K1 Nvidia
K1 DLSS
K1 Videojuegos
K1 Superresolución
K1 Inteligencia Artificial (AI)
K1 Redes Neuronales
K1 Redes Neuronales Convolucionales (CNN)
K1 Deep Learning
K1 Python
K1 TensorFlow
K1 Keras
K1 Nvidia
K1 DLSS
AB Nowadays, a large part of the world's population consumes video games, whether viastreaming or playing. The most consumed kind of game is those of a competitive type, dueto the great impact that eSports have had worldwide. This has caused players to becomeincreasingly concerned about having a better computer, console or smartphone; to be able toperform better and improve as a player. On the other hand, there are players who are lookingto visually enjoy video games, playing at high resolutions and with a high level of graphicdetail. In order to meet these requirements, companies have started looking at ArtificialIntelligence, specifically deep learning techniques, an ally to improve the performance andquality of video games. This report will address one of the features that has recently begunto be implemented in video games, Super-Resolution. This deep learning image processingtechnique allows you to increase the resolution of an image without loss of information.
YR 2019
FD 2019
LK http://riull.ull.es/xmlui/handle/915/16588
UL http://riull.ull.es/xmlui/handle/915/16588
LA es
DS Repositorio institucional de la Universidad de La Laguna
RD 01-mar-2021