“Training a Deep Remastering Model” by Djelouah, Wahlquist, Hattori and Schroers
Conference:
Type(s):
Entry Number: 20
Title:
- Training a Deep Remastering Model
Session/Category Title: Applying Intelligence: AI and ML
Presenter(s)/Author(s):
Abstract:
The success of video streaming platforms has pushed studios to make available TV shows from legacy catalog, and there is an increased demand for remastering this content. Ideally, film reels are re-scanned with modern devices directly into high quality digital format. However this is not always possible as parts of the original film reels can be damaged or missing, and the content is then available in its entirety only in the broadcast version, typically NTSC. In this work, we present a deep learning solution to bring the NTSC version to the new scan quality levels, which would be otherwise impossible with existing tools.
References:
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Victor Cornillere, Abdelaziz Djelouah, Wang Yifan, Olga Sorkine-Hornung, and Christopher Schroers. 2019. Blind image super-resolution with spatially variant degradations. ACM Transactions on Graphics (TOG) 38, 6 (2019), 1–13.Google ScholarDigital Library
Matias Tassano, Julie Delon, and Thomas Veit. 2020. FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimation. In CVPR.Google Scholar
Oliver Wang, Christopher Schroers, Henning Zimmer, Markus Gross, and Alexander Sorkine-Hornung. 2014. Videosnapping: Interactive synchronization of multiple videos. ACM Transactions on Graphics (TOG) 33, 4 (2014), 1–10.Google ScholarDigital Library
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 CVPR Workshops.Google ScholarCross Ref
