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20182022
most citedVIDIT: Virtual Image Dataset for Illumination Transfer

39 citations · 87 across the 9 of their papers we have counts for

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5 papers · 1 filter

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…

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…

eess.IV2020

Stochastic Frequency Masking to Improve Super-Resolution and Denoising Networks

Majed El Helou, Ruofan Zhou, Sabine Süsstrunk

Super-resolution and denoising are ill-posed yet fundamental image restoration tasks. In blind settings, the degradation kernel or the noise level are unknown. This makes restorati…

eess.IV2020

W2S: Microscopy Data with Joint Denoising and Super-Resolution for Widefield to SIM Mapping

Ruofan Zhou, Majed El Helou, Daniel Sage +3

In fluorescence microscopy live-cell imaging, there is a critical trade-off between the signal-to-noise ratio and spatial resolution on one side, and the integrity of the biologica…

eess.IV2019

Image Restoration using Plug-and-Play CNN MAP Denoisers

Siavash Bigdeli, David Honzátko, Sabine Süsstrunk +1

Plug-and-play denoisers can be used to perform generic image restoration tasks independent of the degradation type. These methods build on the fact that the Maximum a Posteriori (M…