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20172022
most citedReducing Anomaly Detection in Images to Detection in Noise

18 citations · 19 across the 4 of their papers we have counts for

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10 papers · 1 filter

cs.CV2022

Improving Pixel-Level Contrastive Learning by Leveraging Exogenous Depth Information

Ahmed Ben Saad, Kristina Prokopetc, Josselin Kherroubi +3

Self-supervised representation learning based on Contrastive Learning (CL) has been the subject of much attention in recent years. This is due to the excellent results obtained on…

cs.CV20221 cited

Self-Supervised Super-Resolution for Multi-Exposure Push-Frame Satellites

Ngoc Long Nguyen, Jérémy Anger, Axel Davy +2

Modern Earth observation satellites capture multi-exposure bursts of push-frame images that can be super-resolved via computational means. In this work, we propose a super-resoluti…

cs.CV2021

Proba-V-ref: Repurposing the Proba-V challenge for reference-aware super resolution

Ngoc Long Nguyen, Jérémy Anger, Axel Davy +2

The PROBA-V Super-Resolution challenge distributes real low-resolution image series and corresponding high-resolution targets to advance research on Multi-Image Super Resolution (M…

cs.CV2020

Self-Supervised training for blind multi-frame video denoising

Valéry Dewil, Jérémy Anger, Axel Davy +3

We propose a self-supervised approach for training multi-frame video denoising networks. These networks predict frame t from a window of frames around t. Our self-supervised approa…

cs.CV2019

Joint Demosaicking and Denoising by Fine-Tuning of Bursts of Raw Images

Thibaud Ehret, Axel Davy, Pablo Arias +1

Demosaicking and denoising are the first steps of any camera image processing pipeline and are key for obtaining high quality RGB images. A promising current research trend aims at…

cs.CV201918 cited

Reducing Anomaly Detection in Images to Detection in Noise

Axel Davy, Thibaud Ehret, Jean-Michel Morel +1

Anomaly detectors address the difficult problem of detecting automatically exceptions in an arbitrary background image. Detection methods have been proposed by the thousands becaus…