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• W3034037160 abstract "Image-based appearance measurements are fundamentally limited in spatial resolution by the acquisition hardware. Due to the ever-increasing resolution of displaying hardware, high-resolution representations of digital material appearance are desireable for authentic renderings. In the present paper, we demonstrate that high-resolution bidirectional texture functions (BTFs) for materials can be obtained from low-resolution measurements using single-image convolutional neural network (CNN) architectures for image super-resolution. In particular, we show that this approach works for high-dynamic-range data and produces consistent BTFs, even though it operates on an image-by-image basis. Moreover, the CNN can be trained on down-sampled measured data, therefore no high-resolution ground-truth data, which would be difficult to obtain, is necessary. We train and test our method's performance on a large-scale BTF database and evaluate against the current state-of-the-art in BTF super-resolution, finding superior performance." @default.
• W3034037160 created "2020-06-12" @default.
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• W3034037160 date "2020-04-01" @default.
• W3034037160 modified "2023-09-23" @default.
• W3034037160 title "Per-Image Super-Resolution for Material BTFs" @default.
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• W3034037160 doi "https://doi.org/10.1109/iccp48838.2020.9105256" @default.
• W3034037160 hasPublicationYear "2020" @default.
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