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Deep Learned Super Resolution for Feature Film Production

Published: 20 August 2020 Publication History

Abstract

Upscaling techniques are commonly used to create high resolution images, which are cost-prohibitive or even impossible to produce otherwise. In recent years, deep learning methods have improved the detail and sharpness of upscaled images over traditional algorithms. Here we discuss the motivation and challenges of bringing deep learned super resolution to production at Pixar, where upscaling is useful for reducing render farm costs and delivering high resolution content.

References

[1]
Steve Bako, Mark Meyer, Tony DeRose, and Pradeep Sen. 2019. Offline Deep Importance Sampling for Monte Carlo Path Tracing. In Computer Graphics Forum, Vol. 38. Wiley Online Library, 527–542.
[2]
Steve Bako, Thijs Vogels, Brian McWilliams, Mark Meyer, Jan Novák, Alex Harvill, Pradeep Sen, Tony Derose, and Fabrice Rousselle. 2017. Kernel-predicting convolutional networks for denoising Monte Carlo renderings.ACM Trans. Graph. 36, 4 (2017), 97–1.
[3]
Yifan Wang, Federico Perazzi, Brian McWilliams, Alexander Sorkine-Hornung, Olga Sorkine-Hornung, and Christopher Schroers. 2018. A fully progressive approach to single-image super-resolution. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 864–873.

Cited By

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  • (2024)PCCFormer: Parallel coupled convolutional transformer for image super-resolutionThe Visual Computer10.1007/s00371-023-03257-340:12(8591-8602)Online publication date: 5-Feb-2024

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Published In

cover image ACM Conferences
SIGGRAPH '20: ACM SIGGRAPH 2020 Talks
August 2020
152 pages
ISBN:9781450379717
DOI:10.1145/3388767
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 August 2020

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Cited By

View all
  • (2024)PCCFormer: Parallel coupled convolutional transformer for image super-resolutionThe Visual Computer10.1007/s00371-023-03257-340:12(8591-8602)Online publication date: 5-Feb-2024

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