no code implementations • 19 Jan 2017 • Martin Rais, Gabriele Facciolo, Enric Meinhardt-Llopis, Jean-Michel Morel, Antoni Buades, Bartomeu Coll
This yields RANSAAC, a framework that improves systematically over RANSAC and its state-of-the-art variants by statistically aggregating hypotheses.
no code implementations • 4 Jun 2018 • Jérémy Anger, Mauricio Delbracio, Gabriele Facciolo
We show that accurately modeling a more realistic image acquisition pipeline leads to significant improvements, both in terms of image quality and PSNR.
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 • 19 Apr 2019 • Jérémy Anger, Carlo de Franchis, Gabriele Facciolo
New micro-satellite constellations enable unprecedented systematic monitoring applications thanks to their wide coverage and short revisit capabilities.
1 code implementation • 19 Apr 2019 • Jérémy Anger, Mauricio Delbracio, Gabriele Facciolo
In this work, we first show that current state-of-the-art kernel estimation methods based on the $\ell_0$ gradient prior can be adapted to handle high noise levels while keeping their efficiency.
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 • 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 • 24 Apr 2020 • Qiyu Jin, Gabriele Facciolo, Jean-Michel Morel
In this paper, we review the main variants of these strategies and carry-out an extensive evaluation to find the best way to reconstruct full color images from a noisy mosaic.
no code implementations • 14 Sep 2020 • Yu Guo, Qiyu Jin, Gabriele Facciolo, Tieyong Zeng, Jean-Michel Morel
Image demosaicking and denoising are the first two key steps of the color image production pipeline.
1 code implementation • 25 Jan 2021 • Ngoc Long Nguyen, Jérémy Anger, Axel Davy, Pablo Arias, Gabriele Facciolo
We argue that in doing so, the challenge ranks the proposed methods not only by their MISR performance, but mainly by the heuristics used to guess which image in the series is the most similar to the high-resolution target.
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.
2 code implementations • IEEE International Geoscience and Remote Sensing Symposium IGARSS 2021 • Roland Akiki, Roger Marí, Carlo de Franchis, Jean-Michel Morel, Gabriele Facciolo
The Rational Polynomial Camera (RPC) model can be used to describe a variety of image acquisition systems in remote sensing, notably optical and Synthetic Aperture Radar (SAR) sensors.
no code implementations • 1 Mar 2021 • Roger Marí, Carlo de Franchis, Enric Meinhardt-Llopis, Gabriele Facciolo
The refined RPCs are then used to reconstruct multiple consistent Digital Surface Models (DSMs) from different stereo pairs at each date.
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 • 25 Apr 2022 • Valéry Dewil, Aranud Barral, Gabriele Facciolo, Pablo Arias
In this paper, we propose a study aiming to determine which is the best approach to train denoising networks for real raw videos: supervision on synthetic realistic data or self-supervision on real data.
no code implementations • CVPR 2022 • Ngoc Long Nguyen, Jérémy Anger, Axel Davy, Pablo Arias, Gabriele Facciolo
Modern Earth observation satellites capture multi-exposure bursts of push-frame images that can be super-resolved via computational means.
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 • 1 Aug 2022 • Thomas Eboli, Jean-Michel Morel, Gabriele Facciolo
The optics of any camera degrades the sharpness of photographs, which is a key visual quality criterion.
no code implementations • 18 Nov 2022 • Ahmed Ben Saad, Kristina Prokopetc, Josselin Kherroubi, Axel Davy, Adrien Courtois, Gabriele Facciolo
In this paper we will focus on the depth information, which can be obtained by using a depth estimation network or measured from available data (stereovision, parallax motion, LiDAR, etc.).
no code implementations • 22 Feb 2023 • Ngoc Long Nguyen, Jérémy Anger, Lara Raad, Bruno Galerne, Gabriele Facciolo
In this work, we study the problem of single-image super-resolution (SISR) of Sentinel-2 imagery.
no code implementations • 10 Mar 2023 • Jamy Lafenetre, Ngoc Long Nguyen, Gabriele Facciolo, Thomas Eboli
Image resolution is an important criterion for many applications based on satellite imagery.
1 code implementation • 14 Apr 2023 • Ngoc Long Nguyen, Jérémy Anger, Axel Davy, Pablo Arias, Gabriele Facciolo
Unfortunately the lack of reliable high-resolution (HR) ground truth limits the application of deep learning methods to this task.
no code implementations • 25 May 2023 • Thomas Eboli, Jean-Michel Morel, Gabriele Facciolo
Blurry images usually exhibit similar blur at various locations across the image domain, a property barely captured in nowadays blind deblurring neural networks.
1 code implementation • 4 Jul 2023 • Franco Marchesoni-Acland, Gabriele Facciolo
This work proposes a strategy for training models while annotating data named Intelligent Annotation (IA).
no code implementations • 4 Jul 2023 • Franco Marchesoni-Acland, Jean-Michel Morel, Josselin Kherroubi, Gabriele Facciolo
The problem is framed as the full annotation of a binary classification dataset with the minimal number of yes/no questions when a predictor is available.
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 • 20 Nov 2023 • Ahmed Ben Saad, Gabriele Facciolo, Axel Davy
In this paper, we highlight the importance of large objects in learning features that are critical for all sizes.
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 • 6 Mar 2024 • Yu Guo, Axel Davy, Gabriele Facciolo, Jean-Michel Morel, Qiyu Jin
In this letter, we propose a solution to both issues by combining a nonlocal algorithm with a lightweight residual CNN.
no code implementations • 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.
1 code implementation • 9 Apr 2024 • Theo Di Piazza, Enric Meinhardt-Llopis, Gabriele Facciolo, Benedicte Bascle, Corentin Abgrall, Jean-Clement Devaux
Here, we demonstrate that the performance of these methods can be significantly enhanced by preprocessing the images to extract their edges, which exhibit robustness to seasonal and illumination variations.