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Cited 0 time in webofscience Cited 5 time in scopus
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AIM 2019 challenge on RAW to RGB mapping: Methods and results

Authors
Ignatov, AndreyTimofte, RaduKo, Sung-JeaKim, Seung-WookUhm, Kwang-HyunJi, Seo-WonCho, Sung-JinHong, Jun-PyoMei, KangfuLi, JunchengZhang, JiajieWu, HaoyuLi, JieHuang, RuiHaris, MuhammadShakhnarovich, GregUkita, NorimichiZhao, YuzhiPo, Lai-ManZhang, TiantianLiao, ZongbangShi, XiangZhang, YujiaOu, WeifengXian, PengfeiXiong, JingjingZhou, ChangYu, Wing YinYubinHou, BingxinPark, BumjunYu, SonghyunKim, SangminJeong, Jechang
Issue Date
Oct-2019
Keywords
AIM; AIM2019; Challenge; Computer vision; Deep learning; Image enhancement; Image manipulation; Mobile cameras; RAW to RGB; Smartphones
Citation
IEEE International Conference on Computer Vision Workshop (ICCVW), pp 3584 - 3590
Pages
7
Indexed
SCOPUS
Journal Title
IEEE International Conference on Computer Vision Workshop (ICCVW)
Start Page
3584
End Page
3590
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/4525
DOI
10.1109/ICCVW.2019.00443
ISSN
2473-9936
2473-9944
Abstract
This paper reviews the first AIM challenge on mapping camera RAW to RGB images with the focus on proposed solutions and results. The participating teams were solving a real-world photo enhancement problem, where the goal was to map the original low-quality RAW images from the Huawei P20 device to the same photos captured with the Canon 5D DSLR camera. The considered problem embraced a number of computer vision subtasks, such as image demosaicing, denoising, gamma correction, image resolution and sharpness enhancement, etc. The target metric used in this challenge combined fidelity scores (PSNR and SSIM) with solutions' perceptual results measured in a user study. The proposed solutions significantly improved baseline results, defining the state-of-the-art for RAW to RGB image restoration.
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서울 공과대학 > 서울 융합전자공학부 > 1. Journal Articles

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