Modelos de Aprendizaje Profundo para el Procesamiento y Clasificación de Imágenes y Vídeo López Tapia, Santiago Molina Soriano, Rafael Katsaggelos, Aggelos K. Universidad de Granada. Programa de Doctorado en Tecnologías de la Información y la Comunicación Deep learning Image processing Video processing The work of Santiago López-Tapia and Rafael Molina was supported by the the Spanish Ministry of Economy and Competitiveness through project DPI2016-77869-C2-2-R and the Visiting Scholar program at the University of Granada. Santiago López-Tapia received financial support through the Spanish FPU program Motivated by the success of DL-based models in image and video problems, in this dissertation, we develop DL-models for challenging image and video formation and interpretation tasks. These are image and video SR, BID, threat detection in PMMWIs and mitosis detection in Whole-Slide Images (WSIs). In this thesis, one common point to all contributions is the use of domain knowledge to improve the solution by developing and applying specialized architectures, regularizations and restrictions. In the next subsections, we present the tasks that we have addressed in this dissertation. Next, we provide a brief introduction to the main DL-based models used in this dissertation: CNNs and Generative Adversarial Networks (GANs). Finally, we present the objectives of this work and the structure of the remainder of this dissertation. 2021-02-12T09:31:50Z 2021-02-12T09:31:50Z 2021 2021-01-29 info:eu-repo/semantics/doctoralThesis López Tapia, Santiago. Modelos de Aprendizaje Profundo para el Procesamiento y Clasificación de Imágenes y Vídeo. Granada: Universidad de Granada, 2021. [http://hdl.handle.net/10481/66493] 9788413067605 http://hdl.handle.net/10481/66493 eng http://creativecommons.org/licenses/by-nc-nd/3.0/es/ info:eu-repo/semantics/openAccess Atribución-NoComercial-SinDerivadas 3.0 España Universidad de Granada