Publication:
Photo Enhancement On Mobile Devices Using Deep Neural Networks

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2019
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2019-09
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In recent years, the return of the usage of Artificial Neural Networks has lead to the greatest improvements in the field of Artificial Intelligence, due to the huge diversity of different applications that deep learning models has in a large variety of research fields, and also the evolution of information processing systems capacity. This thesis aims to study which deep neural networks models are most suitable for photo enhancement, to generate images with certain desired characteristics. Model selection has been done by comparing the both supervised, Convolutional Neural Networks, and unsupervised models, Generative Adversarial Networks. It has been demonstrated that Generative Adversarial Networks have great potential by showing results that compete with the state of the art. The chosen model is a Generative Adversarial model which outperforms the rest in terms of a combination of enhancement quality and time taken in the process. Moreover, since the model is compatible with mobile devices it has been integrated and evaluated in a BQ smartphone, to proof its viability on mobile devices.
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Mejora de imágenes, Redes neuronales, Aplicaciones móviles
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