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• W2969010803 abstract "An omnidirectional image (ODI) enables viewers to look in every direction from a fixed point through a head-mounted display providing an immersive experience compared to that of a standard image. Designing immersive virtual reality systems with ODIs is challenging as they require high resolution content. In this paper, we study super-resolution for ODIs and propose an improved generative adversarial network based model which is optimized to handle the artifacts obtained in the spherical observational space. Specifically, we propose to use a fast PatchGAN discriminator, as it needs fewer parameters and improves the super-resolution at a fine scale. We also explore the generative models with adversarial learning by introducing a spherical-content specific loss function, called 360-SS. To train and test the performance of our proposed model we prepare a dataset of 4500 ODIs. Our results demonstrate the efficacy of the proposed method and identify new challenges in ODI super-resolution for future investigations." @default.
• W2969010803 created "2019-08-22" @default.
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• W2969010803 date "2019-08-12" @default.
• W2969010803 modified "2023-09-27" @default.
• W2969010803 title "Super-resolution of Omnidirectional Images Using Adversarial Learning" @default.
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• W2969010803 hasPublicationYear "2019" @default.
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