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dc.contributor.author
Ignatov, Andrey
dc.contributor.author
Byeoung-Su, Kim
dc.contributor.author
Timofte, Radu
dc.contributor.author
Pouget, Angeline
dc.contributor.author
Song, Fenglong
dc.contributor.author
Li, Cheng
dc.contributor.author
Xiao, Shuai
dc.contributor.author
Fu, Zhongqian
dc.contributor.author
Maggioni, Matteo
dc.contributor.author
Huang, Yibin
dc.contributor.author
Cheng, Shen
dc.contributor.author
Lu, Xin
dc.contributor.author
Zhou, Yifeng
dc.contributor.author
Chen, Liangyu
dc.contributor.author
Liu, Donghao
dc.contributor.author
Zhang, Xiangyu
dc.contributor.author
Fan, Haoqiang
dc.contributor.author
Sun, Jian
dc.contributor.author
Liu, Shuaicheng
dc.contributor.author
Kwon, Minsu
dc.date.accessioned
2021-11-19T16:18:04Z
dc.date.available
2021-11-15T03:53:31Z
dc.date.available
2021-11-19T16:18:04Z
dc.date.issued
2021
dc.identifier.isbn
978-1-6654-4899-4
en_US
dc.identifier.isbn
978-1-6654-4900-7
en_US
dc.identifier.other
10.1109/CVPRW53098.2021.00285
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/515120
dc.description.abstract
Image denoising is one of the most critical problems in mobile photo processing. While many solutions have been proposed for this task, they are usually working with synthetic data and are too computationally expensive to run on mobile devices. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based image denoising solution that can demonstrate high efficiency on smartphone GPUs. For this, the participants were provided with a novel large-scale dataset consisting of noisy-clean image pairs captured in the wild. The runtime of all models was evaluated on the Samsung Exynos 2100 chipset with a powerful Mali GPU capable of accelerating floating-point and quantized neural networks. The proposed solutions are fully compatible with any mobile GPU and are capable of processing 480p resolution images under 40-80 ms while achieving high fidelity results. A detailed description of all models developed in the challenge is provided in this paper. © 2021 IEEE
en_US
dc.language.iso
en
en_US
dc.publisher
IEEE
en_US
dc.title
Fast Camera Image Denoising on Mobile GPUs with Deep Learning, Mobile AI 2021 Challenge: Report
en_US
dc.type
Conference Paper
dc.date.published
2021-09-01
ethz.book.title
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
en_US
ethz.pages.start
2515
en_US
ethz.pages.end
2524
en_US
ethz.event
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
en_US
ethz.event.location
Nashville, TN, USA
en_US
ethz.event.date
June 19-25, 2021
en_US
ethz.identifier.wos
ethz.publication.place
Piscataway, NJ
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02652 - Institut für Bildverarbeitung / Computer Vision Laboratory::03514 - Van Gool, Luc (emeritus) / Van Gool, Luc (emeritus)
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02652 - Institut für Bildverarbeitung / Computer Vision Laboratory::03514 - Van Gool, Luc (emeritus) / Van Gool, Luc (emeritus)
ethz.date.deposited
2021-11-15T03:54:40Z
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2021-11-19T16:18:13Z
ethz.rosetta.lastUpdated
2022-03-29T16:05:24Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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