Now showing 1 - 10 of 13
  • Placeholder Image
    Publication
    NTIRE 2021 depth guided image relighting challenge
    (01-06-2021)
    El Helou, Majed
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    Zhou, Ruofan
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    Susstrunk, Sabine
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    Timofte, Radu
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    Suin, Maitreya
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    Wang, Yuanzhi
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    Lu, Tao
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    Zhang, Yanduo
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    Wu, Yuntao
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    Yang, Hao Hsiang
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    Chen, Wei Ting
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    Kuo, Sy Yen
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    Luo, Hao Lun
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    Zhang, Zhiguang
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    Luo, Zhipeng
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    He, Jianye
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    Zhu, Zuo Liang
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    Li, Zhen
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    Qiu, Jia Xiong
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    Kuang, Zeng Sheng
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    Lu, Cheng Ze
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    Cheng, Ming Ming
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    Shao, Xiu Li
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    Li, Chenghua
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    DIng, Bosong
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    Qian, Wanli
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    Li, Fangya
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    Li, Fu
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    Deng, Ruifeng
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    Lin, Tianwei
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    Liu, Songhua
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    Li, Xin
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    He, Dongliang
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    Yazdani, Amirsaeed
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    Guo, Tiantong
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    Monga, Vishal
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    Nsampi, Ntumba Elie
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    Hu, Zhongyun
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    Wang, Qing
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    Nathan, Sabari
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    Kansal, Priya
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    Zhao, Tongtong
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    Zhao, Shanshan
    Image relighting is attracting increasing interest due to its various applications. From a research perspective, im-age relighting can be exploited to conduct both image normalization for domain adaptation, and also for data augmentation. It also has multiple direct uses for photo montage and aesthetic enhancement. In this paper, we review the NTIRE 2021 depth guided image relighting challenge.We rely on the VIDIT dataset for each of our two challenge tracks, including depth information. The first track is on one-to-one relighting where the goal is to transform the illumination setup of an input image (color temperature and light source position) to the target illumination setup. In the second track, the any-to-any relighting challenge, the objective is to transform the illumination settings of the in-put image to match those of another guide image, similar to style transfer. In both tracks, participants were given depth information about the captured scenes. We had nearly 250 registered participants, leading to 18 confirmed team sub-missions in the final competition stage. The competitions, methods, and final results are presented in this paper.
  • Placeholder Image
    Publication
    AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
    (01-01-2020)
    Zhang, Kai
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    Danelljan, Martin
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    Li, Yawei
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    Timofte, Radu
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    Liu, Jie
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    Tang, Jie
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    Wu, Gangshan
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    Zhu, Yu
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    He, Xiangyu
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    Xu, Wenjie
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    Li, Chenghua
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    Leng, Cong
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    Cheng, Jian
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    Wu, Guangyang
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    Wang, Wenyi
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    Liu, Xiaohong
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    Zhao, Hengyuan
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    Kong, Xiangtao
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    He, Jingwen
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    Qiao, Yu
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    Dong, Chao
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    Luo, Xiaotong
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    Chen, Liang
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    Zhang, Jiangtao
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    Suin, Maitreya
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    Purohit, Kuldeep
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    Li, Xiaochuan
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    Lang, Zhiqiang
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    Nie, Jiangtao
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    Wei, Wei
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    Zhang, Lei
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    Muqeet, Abdul
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    Hwang, Jiwon
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    Yang, Subin
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    Kang, Jung Heum
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    Bae, Sung Ho
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    Kim, Yongwoo
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    Qu, Yanyun
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    Jeon, Geun Woo
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    Choi, Jun Ho
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    Kim, Jun Hyuk
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    Lee, Jong Seok
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    Marty, Steven
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    Marty, Eric
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    Xiong, Dongliang
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    Chen, Siang
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    Zha, Lin
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    Jiang, Jiande
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    Gao, Xinbo
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    Lu, Wen
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    Wang, Haicheng
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    Bhaskara, Vineeth
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    Levinshtein, Alex
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    Tsogkas, Stavros
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    Jepson, Allan
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    Kong, Xiangzhen
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    Zhao, Tongtong
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    Zhao, Shanshan
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    Hrishikesh, P. S.
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    Puthussery, Densen
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    Jiji, C. V.
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    Nan, Nan
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    Liu, Shuai
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    Cai, Jie
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    Meng, Zibo
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    Ding, Jiaming
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    Ho, Chiu Man
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    Wang, Xuehui
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    Yan, Qiong
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    Zhao, Yuzhi
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    Chen, Long
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    Sun, Long
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    Wang, Wenhao
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    Liu, Zhenbing
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    Lan, Rushi
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    Umer, Rao Muhammad
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    Micheloni, Christian
    This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor × 4 based on a set of prior examples of low and corresponding high resolution images. The goal is to devise a network that reduces one or several aspects such as runtime, parameter count, FLOPs, activations, and memory consumption while at least maintaining PSNR of MSRResNet. The track had 150 registered participants, and 25 teams submitted the final results. They gauge the state-of-the-art in efficient single image super-resolution.
  • Placeholder Image
    Publication
    AIM 2019 challenge on bokeh effect synthesis: Methods and results
    (01-10-2019)
    Ignatov, Andrey
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    Patel, Jagruti
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    Timofte, Radu
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    Zheng, Bolun
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    Ye, Xin
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    Huang, Li
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    Tian, Xiang
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    Dutta, Saikat
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    Purohit, Kuldeep
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    Kandula, Praveen
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    Suin, Maitreya
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    Xiong, Zhiwei
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    Huang, Jie
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    Dong, Guanting
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    Yao, Mingde
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    Liu, Dong
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    Yang, Wenjin
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    Hong, Ming
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    Lin, Wenying
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    Qu, Yanyun
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    Choi, Jae Seok
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    Park, Woonsung
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    Kim, Munchurl
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    Liu, Rui
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    Mao, Xiangyu
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    Yang, Chengxi
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    Yan, Qiong
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    Sun, Wenxiu
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    Fang, Junkai
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    Shang, Meimei
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    Gao, Fei
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    Ghosh, Sujoy
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    Sharma, Prasen Kumar
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    Sur, Arijit
    This paper reviews the first AIM challenge on bokeh effect synthesis with the focus on proposed solutions and results. The participating teams were solving a real-world image-to-image mapping problem, where the goal was to map standard narrow-aperture photos to the same photos captured with a shallow depth-of-field by the Canon 70D DSLR camera. In this task, the participants had to restore bokeh effect based on only one single frame without any additional data from other cameras or sensors. 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 practical bokeh effect simulation.
  • Placeholder Image
    Publication
    NTIRE 2019 challenge on video deblurring: Methods and results
    (01-06-2019)
    Nah, Seungjun
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    Timofte, Radu
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    Baik, Sungyong
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    Hong, Seokil
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    Moon, Gyeongsik
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    Son, Sanghyun
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    Lee, Kyoung Mu
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    Wang, Xintao
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    Chan, Kelvin C.K.
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    Yu, Ke
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    Dong, Chao
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    Loy, Chen Change
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    Fan, Yuchen
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    Yu, Jiahui
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    Liu, DIng
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    Huang, Thomas S.
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    Sim, Hyeonjun
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    Kim, Munchurl
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    Park, Dongwon
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    Kim, Jisoo
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    Chun, Se Young
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    Haris, Muhammad
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    Shakhnarovich, Greg
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    Ukita, Norimichi
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    Zamir, Syed Waqas
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    Arora, Aditya
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    Khan, Salman
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    Khan, Fahad Shahbaz
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    Shao, Ling
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    Gupta, Rahul Kumar
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    Chudasama, Vishal
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    Patel, Heena
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    Upla, Kishor
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    Fan, Hongfei
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    Li, Guo
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    Zhang, Yumei
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    Li, Xiang
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    Zhang, Wenjie
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    He, Qingwen
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    Purohit, Kuldeep
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    Kim, Jeonghun
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    Tofighi, Mohammad
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    Guo, Tiantong
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    Monga, Vishal
    This paper reviews the first NTIRE challenge on video deblurring (restoration of rich details and high frequency components from blurred video frames) with focus on the proposed solutions and results. A new REalistic and Diverse Scenes dataset (REDS) was employed. The challenge was divided into 2 tracks. Track 1 employed dynamic motion blurs while Track 2 had additional MPEG video compression artifacts. Each competition had 109 and 93 registered participants. Total 13 teams competed in the final testing phase. They gauge the state-of-the-art in video deblurring problem.
  • Placeholder Image
    Publication
    AIM 2019 challenge on real-world image super-resolution: Methods and results
    (01-10-2019)
    Lugmayr, Andreas
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    Danelljan, Martin
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    Timofte, Radu
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    Fritsche, Manuel
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    Gu, Shuhang
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    Purohit, Kuldeep
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    Kandula, Praveen
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    Suin, Maitreya
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    Joon, Nam Hyung
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    Won, Yu Seung
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    Kim, Guisik
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    Kwon, Dokyeong
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    Hsu, Chih Chung
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    Lin, Chia Hsiang
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    Huang, Yuanfei
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    Sun, Xiaopeng
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    Lu, Wen
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    Li, Jie
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    Gao, Xinbo
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    Bell-Kligler, Sefi
    This paper reviews the AIM 2019 challenge on real world super-resolution. It focuses on the participating methods and final results. The challenge addresses the real world setting, where paired true high and low-resolution images are unavailable. For training, only one set of source input images is therefore provided in the challenge. In Track 1: Source Domain the aim is to super-resolve such images while preserving the low level image characteristics of the source input domain. In Track 2: Target Domain a set of high-quality images is also provided for training, that defines the output domain and desired quality of the super-resolved images. To allow for quantitative evaluation, the source input images in both tracks are constructed using artificial, but realistic, image degradations. The challenge is the first of its kind, aiming to advance the state-of-the-art and provide a standard benchmark for this newly emerging task. In total 7 teams competed in the final testing phase, demonstrating new and innovative solutions to the problem.
  • Placeholder Image
    Publication
    NTIRE 2019 challenge on image colorization: Report
    (01-06-2019)
    Nah, Seungjun
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    Timofte, Radu
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    Zhang, Richard
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    Suin, Maitreya
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    Purohit, Kuldeep
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    Athi Narayanan, S.
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    Pinjari, Jameer Babu
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    Xiong, Zhiwei
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    Shi, Zhan
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    Chen, Chang
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    Liu, Dong
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    Sharma, Manoj
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    Makwana, Megh
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    Badhwar, Anuj
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    Singh, Ajay Pratap
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    Upadhyay, Avinash
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    Trivedi, Akkshita
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    Saini, Anil
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    Chaudhury, Santanu
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    Sharma, Prasen Kumar
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    Jain, Priyankar
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    Sur, Arijit
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    Ozbulak, Gokhan
    This paper reviews the NTIRE challenge on image colorization (estimating color information from the corresponding gray image) with focus on proposed solutions and results. It is the first challenge of its kind. The challenge had 2 tracks. Track 1 takes a single gray image as input. In Track 2, in addition to the gray input image, some color seeds (randomly samples from the latent color image) are also provided for guiding the colorization process. The operators were learnable through provided pairs of gray and color training images. The tracks had 188 registered participants, and 8 teams competed in the final testing phase.
  • Placeholder Image
    Publication
    AIM 2020 Challenge on Image Extreme Inpainting
    (01-01-2020)
    Ntavelis, Evangelos
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    Romero, Andrés
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    Bigdeli, Siavash
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    Timofte, Radu
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    Hui, Zheng
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    Wang, Xiumei
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    Gao, Xinbo
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    Shin, Chajin
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    Kim, Taeoh
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    Son, Hanbin
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    Lee, Sangyoun
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    Li, Chao
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    Li, Fu
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    He, Dongliang
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    Wen, Shilei
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    Ding, Errui
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    Bai, Mengmeng
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    Li, Shuchen
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    Zeng, Yu
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    Lin, Zhe
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    Yang, Jimei
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    Zhang, Jianming
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    Shechtman, Eli
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    Lu, Huchuan
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    Zeng, Weijian
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    Ni, Haopeng
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    Cai, Yiyang
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    Li, Chenghua
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    Xu, Dejia
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    Wu, Haoning
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    Han, Yu
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    Nadim, Uddin S.M.
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    Jang, Hae Woong
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    Ahmed, Soikat Hasan
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    Yoon, Jungmin
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    Jung, Yong Ju
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    Li, Chu Tak
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    Liu, Zhi Song
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    Wang, Li Wen
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    Siu, Wan Chi
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    Lun, Daniel P.K.
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    Suin, Maitreya
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    Purohit, Kuldeep
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    Narang, Pratik
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    Mandal, Murari
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    Chauhan, Pranjal Singh
    This paper reviews the AIM 2020 challenge on extreme image inpainting. This report focuses on proposed solutions and results for two different tracks on extreme image inpainting: classical image inpainting and semantically guided image inpainting. The goal of track 1 is to inpaint large part of the image with no supervision. Similarly, the goal of track 2 is to inpaint the image by having access to the entire semantic segmentation map of the input. The challenge had 88 and 74 participants, respectively. 11 and 6 teams competed in the final phase of the challenge, respectively. This report gauges current solutions and set a benchmark for future extreme image inpainting methods.
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    Publication
    NTIRE 2019 challenge on video super-resolution: Methods and results
    (01-06-2019)
    Nah, Seungjun
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    Timofte, Radu
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    Gu, Shuhang
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    Baik, Sungyong
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    Hong, Seokil
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    Moon, Gyeongsik
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    Son, Sanghyun
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    Lee, Kyoung Mu
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    Wang, Xintao
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    Chan, Kelvin C.K.
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    Yu, Ke
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    Dong, Chao
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    Loy, Chen Change
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    Fan, Yuchen
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    Yu, Jiahui
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    Liu, DIng
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    Huang, Thomas S.
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    Liu, Xiao
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    Li, Chao
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    He, Dongliang
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    DIng, Yukang
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    Wen, Shilei
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    Porikli, Fatih
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    Kalarot, Ratheesh
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    Haris, Muhammad
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    Shakhnarovich, Greg
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    Ukita, Norimichi
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    Yi, Peng
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    Wang, Zhongyuan
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    Jiang, Kui
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    Jiang, Junjun
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    Ma, Jiayi
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    Dong, Hang
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    Zhang, Xinyi
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    Hu, Zhe
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    Kim, Kwanyoung
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    Kang, Dong Un
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    Chun, Se Young
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    Purohit, Kuldeep
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    Tian, Yapeng
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    Zhang, Yulun
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    Fu, Yun
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    Xu, Chenliang
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    Tekalp, A. Murat
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    Yilmaz, M. Akin
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    Korkmaz, Cansu
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    Sharma, Manoj
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    Makwana, Megh
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    Badhwar, Anuj
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    Singh, Ajay Pratap
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    Upadhyay, Avinash
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    Mukhopadhyay, Rudrabha
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    Shukla, Ankit
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    Khanna, Dheeraj
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    Mandal, A. S.
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    Chaudhury, Santanu
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    Miao, Si
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    Zhu, Yongxin
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    Huo, Xiao
    This paper reviews the first NTIRE challenge on video super-resolution (restoration of rich details in low-resolution video frames) with focus on proposed solutions and results. A new REalistic and Diverse Scenes dataset (REDS) was employed. The challenge was divided into 2 tracks. Track 1 employed standard bicubic downscaling setup while Track 2 had realistic dynamic motion blurs. Each competition had 124 and 104 registered participants. There were total 14 teams in the final testing phase. They gauge the state-of-the-art in video super-resolution.
  • Placeholder Image
    Publication
    PIRM challenge on perceptual image enhancement on smartphones: Report
    (01-01-2019)
    Ignatov, Andrey
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    Timofte, Radu
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    Van Vu, Thang
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    Luu, Tung Minh
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    Pham, Trung X.
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    Van Nguyen, Cao
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    Kim, Yongwoo
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    Choi, Jae Seok
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    Kim, Munchurl
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    Huang, Jie
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    Ran, Jiewen
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    Xing, Chen
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    Zhou, Xingguang
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    Zhu, Pengfei
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    Geng, Mingrui
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    Li, Yawei
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    Agustsson, Eirikur
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    Gu, Shuhang
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    Van Gool, Luc
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    de Stoutz, Etienne
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    Kobyshev, Nikolay
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    Nie, Kehui
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    Zhao, Yan
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    Li, Gen
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    Tong, Tong
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    Gao, Qinquan
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    Hanwen, Liu
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    Michelini, Pablo Navarrete
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    Dan, Zhu
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    Fengshuo, Hu
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    Hui, Zheng
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    Wang, Xiumei
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    Deng, Lirui
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    Meng, Rang
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    Qin, Jinghui
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    Shi, Yukai
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    Wen, Wushao
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    Lin, Liang
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    Feng, Ruicheng
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    Wu, Shixiang
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    Dong, Chao
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    Qiao, Yu
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    Vasu, Subeesh
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    Thekke Madam, Nimisha
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    Kandula, Praveen
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    Liu, Jie
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    Jung, Cheolkon
    This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones. The challenge consisted of two tracks. In the first one, participants were solving the classical image super-resolution problem with a bicubic downscaling factor of 4. The second track was aimed at real-world photo enhancement, and the goal was to map low-quality photos from the iPhone 3GS device to the same photos captured with a DSLR camera. The target metric used in this challenge combined the runtime, PSNR scores and solutions’ perceptual results measured in the user study. To ensure the efficiency of the submitted models, we additionally measured their runtime and memory requirements on Android smartphones. The proposed solutions significantly improved baseline results defining the state-of-the-art for image enhancement on smartphones.
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    Publication
    AIM 2020 Challenge on Rendering Realistic Bokeh
    (01-01-2020)
    Ignatov, Andrey
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    Timofte, Radu
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    Qian, Ming
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    Qiao, Congyu
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    Lin, Jiamin
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    Guo, Zhenyu
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    Li, Chenghua
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    Leng, Cong
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    Cheng, Jian
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    Peng, Juewen
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    Luo, Xianrui
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    Xian, Ke
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    Wu, Zijin
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    Cao, Zhiguo
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    Puthussery, Densen
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    Jiji, C. V.
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    Hrishikesh, P. S.
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    Kuriakose, Melvin
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    Dutta, Saikat
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    Das, Sourya Dipta
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    Shah, Nisarg A.
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    Purohit, Kuldeep
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    Kandula, Praveen
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    Suin, Maitreya
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    Saagara, M. B.
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    Minnu, A. L.
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    Sanjana, A. R.
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    Praseeda, S.
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    Wu, Ge
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    Chen, Xueqin
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    Wang, Tengyao
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    Zheng, Max
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    Wong, Hulk
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    Zou, Jay
    This paper reviews the second AIM realistic bokeh effect rendering challenge and provides the description of the proposed solutions and results. The participating teams were solving a real-world bokeh simulation problem, where the goal was to learn a realistic shallow focus technique using a large-scale EBB! bokeh dataset consisting of 5K shallow/wide depth-of-field image pairs captured using the Canon 7D DSLR camera. The participants had to render bokeh effect based on only one single frame without any additional data from other cameras or sensors. The target metric used in this challenge combined the runtime and the perceptual quality of the solutions measured in the user study. To ensure the efficiency of the submitted models, we measured their runtime on standard desktop CPUs as well as were running the models on smartphone GPUs. The proposed solutions significantly improved the baseline results, defining the state-of-the-art for practical bokeh effect rendering problem.