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dc.contributor.advisorEbrahimi, Mehran
dc.contributor.authorAbed jooy Divshali, Aref
dc.date.accessioned2022-04-26T15:37:43Z
dc.date.accessioned2022-06-15T15:31:35Z
dc.date.available2022-04-26T15:37:43Z
dc.date.available2022-06-15T15:31:35Z
dc.date.issued2022-04-01
dc.identifier.urihttps://hdl.handle.net/10155/1437
dc.description.abstractRecently, deep learning methods specifically generative adversarial networks (GANs) have been used to rapidly improve a wide range of image enhancement tasks including image inpainting and image resolution enhancement also known as super-resolution. Image-to-image translation methods convert an image provided in a source modality (e.g., a nighttime image) to an image of a target modality (e.g., a daytime image) by learning an image generation function. These methods can be applied to a wide variety of problems in image processing and computer vision. The use of GANs for image-to-image translation has also been extensively studied. We propose the problem of combining the image-enhancement tasks (e.g., image inpainting or super-resolution) with the image-to-image translation task in a joint formulation. Given a distorted nighttime image of a scene can one recover a restored daytime image of the same scene? Two models to address the joint problem will be presented. Our models are validated on night-to-day joint image translation and enhancement for both super-resolution and inpainting. Promising qualitative and quantitative results will be reported.en
dc.description.sponsorshipUniversity of Ontario Institute of Technologyen
dc.language.isoenen
dc.subjectImage inpaintingen
dc.subjectImage-to-image translationen
dc.subjectImage super-resolutionen
dc.subjectGenerative Adversarial Network (GAN)en
dc.titleGenerative models for multi-modality image inpainting and resolution enhancementen
dc.typeThesisen
dc.degree.levelMaster of Science (MSc)en
dc.degree.disciplineComputer Scienceen


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