Detailed Information

Cited 0 time in webofscience Cited 5 time in scopus
Metadata Downloads

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
Publisher
Institute of Electrical and Electronics Engineers Inc.
Keywords
AIM; AIM2019; Challenge; Computer vision; Deep learning; Image enhancement; Image manipulation; Mobile cameras; RAW to RGB; Smartphones
Citation
Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019, pp.3584 - 3590
Indexed
SCOPUS
Journal Title
Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019
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
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.
Files in This Item
Go to Link
Appears in
Collections
서울 공과대학 > 서울 융합전자공학부 > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Jeong, Jechang photo

Jeong, Jechang
COLLEGE OF ENGINEERING (SCHOOL OF ELECTRONIC ENGINEERING)
Read more

Altmetrics

Total Views & Downloads

BROWSE