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Face Hallucination Using Generative Adversarial Networks

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dc.contributor.advisor Dailey, Matthew N.
dc.contributor.author Raju, N. Prithvi
dc.contributor.other Ekpanyapong, Mongkol
dc.contributor.other Taparugssanagorn, Attaphongse
dc.date.accessioned 2020-05-13T06:29:29Z
dc.date.available 2020-05-13T06:29:29Z
dc.date.issued 2020-05
dc.identifier.other AIT
dc.identifier.uri http://www.cs.ait.ac.th/xmlui/handle/123456789/968
dc.description.abstract The evolution of surveillance techniques in the modern era has powered AI to develop algorithms that provide numerous opportunities to improve security and well being. Surveillance cameras monitoring a wide field of view can result in capturing indiscernible faces which makes human activity monitoring and recognition impossible. Single Image Super-resolution (SISR) provides a feasible solution to enhance such faces in order to recognise the individuals. However, lack of ground truth high-resolution methods make it infeasible to develop such devised methods to develop a standard SISR model. In this research, I explore the challenge of building a SISR system from a limited set of unaligned pairs of Low-res and High-res images. Though none of the models could produce substantial results, each one of those unravel problems in an unsupervised setting. I propose a model that leverages literature from Style Transfer to achieve the reconstruction of noisy low-res face images, but in an extremely specific domain. I further conclude the research for exploration of models and domains in Deep Learning where we might find the possible solution to the problem. en_US
dc.language.iso en_US en_US
dc.publisher AIT en_US
dc.subject Superresolution, Face Hallucination, CycleGAN, Deep Generative Models, Style Transfer en_US
dc.title Face Hallucination Using Generative Adversarial Networks en_US
dc.type Research report en_US


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