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dc.contributor.authorZhang, Yangen_US
dc.contributor.authorAydin, Tunc O.en_US
dc.contributor.editorMitra, Niloy and Viola, Ivanen_US
dc.date.accessioned2021-04-09T08:00:10Z
dc.date.available2021-04-09T08:00:10Z
dc.date.issued2021
dc.identifier.issn1467-8659
dc.identifier.urihttps://doi.org/10.1111/cgf.142624
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf142624
dc.description.abstractWe study the problem of High Dynamic Range (HDR) image reconstruction from a Standard Dynamic Range (SDR) input with potential clipping artifacts. Instead of building a direct model that maps from SDR to HDR images as in previous work, we decompose an input SDR image into a base (low frequency) and detail layer (high frequency), and treat reconstructing these two layers as two separate problems. We propose a novel architecture that comprises individual components specially designed to handle both tasks. Specifically, our base layer reconstruction component recovers low frequency content and remaps the color gamut of the input SDR, whereas our detail layer reconstruction component, which builds upon prior work on image inpainting, hallucinates missing texture information. The output HDR prediction is produced by a final refinement stage. We present qualitative and quantitative comparisons with existing techniques where our method achieves state-of-the-art performance.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.titleDeep HDR Estimation with Generative Detail Reconstructionen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersDeep Rendering
dc.description.volume40
dc.description.number2
dc.identifier.doi10.1111/cgf.142624
dc.identifier.pages179-190


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