1 code implementation • 8 Mar 2024 • Xavier Bou, Gabriele Facciolo, Rafael Grompone von Gioi, Jean-Michel Morel, Thibaud Ehret
Moreover, we study the performance of both visual and image-text features, namely DINOv2 and CLIP, including two CLIP models specifically tailored for remote sensing applications.
no code implementations • 6 Mar 2024 • Xavier Bou, Thibaud Ehret, Rafael Grompone von Gioi, Jeremy Anger
Identifying flood affected areas in remote sensing data is a critical problem in earth observation to analyze flood impact and drive responses.
no code implementations • 20 Dec 2023 • Thibaud Ehret, Roger Marí, Dawa Derksen, Nicolas Gasnier, Gabriele Facciolo
Radiance fields have been a major breakthrough in the field of inverse rendering, novel view synthesis and 3D modeling of complex scenes from multi-view image collections.
no code implementations • 9 Jul 2023 • Xavier Bou, Aitor Artola, Thibaud Ehret, Gabriele Facciolo, Jean-Michel Morel, Rafael Grompone von Gioi
Experimental results reveal that the proposed a-contrario validation is able to largely reduce the number of false alarms at both pixel and object levels.
no code implementations • 17 Nov 2022 • Alexis Groshenry, Clement Giron, Thomas Lauvaux, Alexandre d'Aspremont, Thibaud Ehret
The new generation of hyperspectral imagers, such as PRISMA, has improved significantly our detection capability of methane (CH4) plumes from space at high spatial resolution (30m).
no code implementations • 29 Jun 2022 • Thibaud Ehret, Roger Marí, Gabriele Facciolo
Neural radiance fields, or NeRF, represent a breakthrough in the field of novel view synthesis and 3D modeling of complex scenes from multi-view image collections.
no code implementations • 16 Mar 2022 • Roger Marí, Gabriele Facciolo, Thibaud Ehret
We introduce the Satellite Neural Radiance Field (Sat-NeRF), a new end-to-end model for learning multi-view satellite photogrammetry in the wild.
no code implementations • 3 Feb 2021 • Jérémy Anger, Thibaud Ehret, Gabriele Facciolo
Recent constellations of satellites, including the Skysat constellation, are able to acquire bursts of images.
no code implementations • 15 Apr 2020 • Valéry Dewil, Jérémy Anger, Axel Davy, Thibaud Ehret, Pablo Arias, Gabriele Facciolo
We propose a self-supervised approach for training multi-frame video denoising networks.
no code implementations • Journal of Real-Time Image Processing (2021) 18:57–74 2020 • Axel Davy, Thibaud Ehret
Denoising is an essential part of any image- or video-processing pipeline.
1 code implementation • 6 Jan 2020 • Thibaud Ehret, Pablo Arias
VBM3D is an extension to video of the well known image denoising algorithm BM3D, which takes advantage of the sparse representation of stacks of similar patches in a transform domain.
no code implementations • 3 Jun 2019 • Thibaud Ehret
Detecting reliably copy-move forgeries is difficult because images do contain similar objects.
1 code implementation • ICCV 2019 • Thibaud Ehret, Axel Davy, Pablo Arias, Gabriele Facciolo
Due to the unavailability of ground truth data these networks cannot be currently trained using real RAW images.
no code implementations • 25 Apr 2019 • Axel Davy, Thibaud Ehret, Jean-Michel Morel, Mauricio Delbracio
Anomaly detectors address the difficult problem of detecting automatically exceptions in an arbitrary background image.
2 code implementations • 30 Nov 2018 • Axel Davy, Thibaud Ehret, Jean-Michel Morel, Pablo Arias, Gabriele Facciolo
To the best of our knowledge, this is the first successful application of a CNN to video denoising.
1 code implementation • CVPR 2019 • Thibaud Ehret, Axel Davy, Jean-Michel Morel, Gabriele Facciolo, Pablo Arias
Modeling the processing chain that has produced a video is a difficult reverse engineering task, even when the camera is available.
no code implementations • 7 Aug 2018 • Thibaud Ehret, Axel Davy, Jean-Michel Morel, Mauricio Delbracio
We review the broad variety of methods that have been proposed for anomaly detection in images.
no code implementations • CVPR 2018 • Thibaud Ehret, Pablo Arias
We also derive more specific bounds for two of these particular cases: the original PatchMatch and Coherency Sensitive Hashing.