Naturalness-Preserving Image Tone Enhancement Using Generative Adversarial Networks

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dc.contributor.authorSon, Hyeongseoken_US
dc.contributor.authorLee, Gunheeen_US
dc.contributor.authorCho, Sunghyunen_US
dc.contributor.authorLee, Seungyongen_US
dc.contributor.editorLee, Jehee and Theobalt, Christian and Wetzstein, Gordonen_US
dc.date.accessioned2019-10-14T05:07:27Z
dc.date.available2019-10-14T05:07:27Z
dc.date.issued2019
dc.description.abstractThis paper proposes a deep learning-based image tone enhancement approach that can maximally enhance the tone of an image while preserving the naturalness. Our approach does not require carefully generated ground-truth images by human experts for training. Instead, we train a deep neural network to mimic the behavior of a previous classical filtering method that produces drastic but possibly unnatural-looking tone enhancement results. To preserve the naturalness, we adopt the generative adversarial network (GAN) framework as a regularizer for the naturalness. To suppress artifacts caused by the generative nature of the GAN framework, we also propose an imbalanced cycle-consistency loss. Experimental results show that our approach can effectively enhance the tone and contrast of an image while preserving the naturalness compared to previous state-of-the-art approaches.en_US
dc.description.number7
dc.description.sectionheadersComputational Photography
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume38
dc.identifier.doi10.1111/cgf.13836
dc.identifier.issn1467-8659
dc.identifier.pages277-285
dc.identifier.urihttps://doi.org/10.1111/cgf.13836
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13836
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectComputing methodologies
dc.subjectImage processing
dc.titleNaturalness-Preserving Image Tone Enhancement Using Generative Adversarial Networksen_US
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