Perception-enhanced image super-resolution via relativistic generative adversarial networks

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dc.contributor.authorVu, Thangko
dc.contributor.authorLuu, Tung Mko
dc.contributor.authorYoo, Chang-Dongko
dc.date.accessioned2023-08-24T10:00:19Z-
dc.date.available2023-08-24T10:00:19Z-
dc.date.created2023-07-06-
dc.date.issued2019-09-
dc.identifier.citation15th European Conference on Computer Vision, ECCV 2018, pp.98 - 113-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10203/311798-
dc.description.abstractThis paper considers a deep Generative Adversarial Networks (GAN) based method referred to as the Perception-Enhanced Super-Resolution (PESR) for Single Image Super Resolution (SISR) that enhances the perceptual quality of the reconstructed images by considering the following three issues: (1) ease GAN training by replacing an absolute with a relativistic discriminator, (2) include in the loss function a mechanism to emphasize difficult training samples which are generally rich in texture and (3) provide a flexible quality control scheme at test time to trade-off between perception and fidelity. Based on extensive experiments on six benchmark datasets, PESR outperforms recent state-of-the-art SISR methods in terms of perceptual quality. The code is available at https://github.com/thangvubk/PESR.-
dc.languageEnglish-
dc.publisherSpringer Verlag-
dc.titlePerception-enhanced image super-resolution via relativistic generative adversarial networks-
dc.typeConference-
dc.identifier.wosid000594394700007-
dc.identifier.scopusid2-s2.0-85061724416-
dc.type.rimsCONF-
dc.citation.beginningpage98-
dc.citation.endingpage113-
dc.citation.publicationname15th European Conference on Computer Vision, ECCV 2018-
dc.identifier.conferencecountryGE-
dc.identifier.conferencelocationMunich-
dc.identifier.doi10.1007/978-3-030-11021-5_7-
dc.contributor.localauthorYoo, Chang-Dong-
dc.contributor.nonIdAuthorLuu, Tung M-
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EE-Conference Papers(학술회의논문)
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