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Neural Super-Resolution in Real-Time Rendering Using Auxiliary Feature Enhancement

Neural Super-Resolution in Real-Time Rendering Using Auxiliary Feature Enhancement

Zhihua Zhong, Guanlin Chen, Rui Wang, Yuchi Huo
Copyright: © 2023 |Volume: 34 |Issue: 3 |Pages: 13
ISSN: 1063-8016|EISSN: 1533-8010|EISBN13: 9798369301517|DOI: 10.4018/JDM.321544
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MLA

Zhong, Zhihua, et al. "Neural Super-Resolution in Real-Time Rendering Using Auxiliary Feature Enhancement." JDM vol.34, no.3 2023: pp.1-13. https://doi.org/10.4018/JDM.321544

APA

Zhong, Z., Chen, G., Wang, R., & Huo, Y. (2023). Neural Super-Resolution in Real-Time Rendering Using Auxiliary Feature Enhancement. Journal of Database Management (JDM), 34(3), 1-13. https://doi.org/10.4018/JDM.321544

Chicago

Zhong, Zhihua, et al. "Neural Super-Resolution in Real-Time Rendering Using Auxiliary Feature Enhancement," Journal of Database Management (JDM) 34, no.3: 1-13. https://doi.org/10.4018/JDM.321544

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Abstract

As the demand for high quality and high resolution in real-time rendering grows, superresolution is on its way to becoming a necessary component in modern real-time rendering applications (e.g., video games). The superresolution technique allows graphic applications to save computational costs by rendering at a lower resolution and reconstructing a high-resolution result. Nvidia introduced DLSS to the market as the first superresolution application in 2020, and NSRR was published on Siggraph the same year. Each of these approaches has shown powerful capabilities and is well suited to the needs of the industrial sector. In this paper, the authors propose the optimization potential of superresolution algorithms by introducing feature enhancement and feature caching modules and attempt to improve the current algorithms.