activity
20182022
most citedVIDIT: Virtual Image Dataset for Illumination Transfer

39 citations · 56 across the 7 of their papers we have counts for

collaborators

12 papers

eess.IV2022

Image Denoising with Control over Deep Network Hallucination

Qiyuan Liang, Florian Cassayre, Haley Owsianko +2

Deep image denoisers achieve state-of-the-art results but with a hidden cost. As witnessed in recent literature, these deep networks are capable of overfitting their training distr…

cs.CV20211 cited

Fidelity Estimation Improves Noisy-Image Classification With Pretrained Networks

Xiaoyu Lin, Deblina Bhattacharjee, Majed El Helou +1

Image classification has significantly improved using deep learning. This is mainly due to convolutional neural networks (CNNs) that are capable of learning rich feature extractors…

eess.IV2021

Deep Gaussian Denoiser Epistemic Uncertainty and Decoupled Dual-Attention Fusion

Xiaoqi Ma, Xiaoyu Lin, Majed El Helou +1

Following the performance breakthrough of denoising networks, improvements have come chiefly through novel architecture designs and increased depth. While novel denoising networks…

cs.CV202014 cited

AIM 2020: Scene Relighting and Illumination Estimation Challenge

Majed El Helou, Ruofan Zhou, Sabine Süsstrunk +34

We review the AIM 2020 challenge on virtual image relighting and illumination estimation. This paper presents the novel VIDIT dataset used in the challenge and the different propos…

cs.CV202039 cited

VIDIT: Virtual Image Dataset for Illumination Transfer

Majed El Helou, Ruofan Zhou, Johan Barthas +1

Deep image relighting is gaining more interest lately, as it allows photo enhancement through illumination-specific retouching without human effort. Aside from aesthetic enhancemen…

cs.RO20201 cited

Realizability of Planar Point Embeddings from Angle Measurements

Frederike Dümbgen, Majed El Helou, Adam Scholefield

Localization of a set of nodes is an important and a thoroughly researched problem in robotics and sensor networks. This paper is concerned with the theory of localization from inn…