M
Majed El Helou
Researcher at École Polytechnique Fédérale de Lausanne
Publications - 36
Citations - 453
Majed El Helou is an academic researcher from École Polytechnique Fédérale de Lausanne. The author has contributed to research in topics: Deep learning & Overfitting. The author has an hindex of 10, co-authored 32 publications receiving 253 citations. Previous affiliations of Majed El Helou include Disney Research & École Normale Supérieure.
Papers
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Journal ArticleDOI
Blind Universal Bayesian Image Denoising With Gaussian Noise Level Learning
Majed El Helou,Sabine Süsstrunk +1 more
TL;DR: This work proposes a theoretically-grounded blind and universal deep learning image denoiser for additive Gaussian noise removal, based on an optimal denoising solution, which it is derived theoretically with a Gaussian image prior assumption.
Posted Content
VIDIT: Virtual Image Dataset for Illumination Transfer
TL;DR: This work presents a novel dataset, the Virtual Image Dataset for Illumination Transfer (VIDIT), in an effort to create a reference evaluation benchmark and to push forward the development of illumination manipulation methods.
Book ChapterDOI
AIM 2020: Scene Relighting and Illumination Estimation Challenge
Majed El Helou,Ruofan Zhou,Sabine Süsstrunk,Radu Timofte,Mahmoud Afifi,Michael S. Brown,Kele Xu,Hengxing Cai,Yuzhong Liu,Li-Wen Wang,Zhi-Song Liu,Chu-Tak Li,Sourya Dipta Das,Nisarg Shah,Akashdeep Jassal,Tongtong Zhao,Shanshan Zhao,Sabari Nathan,M. Parisa Beham,R. Suganya,Qing Wang,Zhongyun Hu,Xin Huang,Yaning Li,Maitreya Suin,Kuldeep Purohit,A. N. Rajagopalan,Densen Puthussery,P. S. Hrishikesh,Melvin Kuriakose,C. V. Jiji,Yu Zhu,Liping Dong,Zhuolong Jiang,Chenghua Li,Cong Leng,Jian Cheng +36 more
TL;DR: The AIM 2020 challenge on virtual image relighting and illumination estimation as discussed by the authors focused on one-to-one relighting, where the objective was to relight an input photo of a scene with a different color temperature and illuminant orientation.
Journal ArticleDOI
Blind Universal Bayesian Image Denoising with Gaussian Noise Level Learning
Majed El Helou,Sabine Süsstrunk +1 more
TL;DR: In this article, the authors proposed a theoretically grounded blind and universal deep learning image denoiser for additive Gaussian noise removal, which is based on an optimal denoising solution.
Proceedings ArticleDOI
NTIRE 2021 Depth Guided Image Relighting Challenge
Majed El Helou,Ruofan Zhou,Sabine Süsstrunk,Radu Timofte,Maitreya Suin,A. N. Rajagopalan,Yuanzhi Wang,Tao Lu,Yanduo Zhang,Yuntao Wu,Hao-Hsiang Yang,Wei-Ting Chen,Sy-Yen Kuo,Hao-Lun Luo,Zhiguang Zhang,Zhipeng Luo,Jianye He,Zuo-Liang Zhu,Zhen Li,Jia-Xiong Qiu,Zeng-Sheng Kuang,Cheng-Ze Lu,Ming-Ming Cheng,Xiu-Li Shao,Chenghua Li,B. Z. Ding,Wanli Qian,Fangya Li,Fu Li,Ruifeng Deng,Tianwei Lin,Songhua Liu,Li Xin,Dongliang He,Amirsaeed Yazdani,Tiantong Guo,Vishal Monga,Ntumba Elie Nsampi,Zhongyun Hu,Qing Wang,Sabari Nathan,Priya Kansal,Tongtong Zhao,Shanshan Zhao +43 more
TL;DR: The NTIRE 2021 depth guided image relighting challenge as mentioned in this paper focused 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.