29 citations · 34 across the 12 of their papers we have counts for
15 papers
SeasonStereo: Robust Dense Stereo Matching for Multi-Date Satellite Imagery via Generative AI
Álvaro Díaz-Laureano, Roger Marí, Elías Masquil +2
Accurate 3D reconstruction from satellite imagery typically relies on near-simultaneous stereo pairs, limiting its applicability to diachronic settings where multi-date images exhi…
Self-Supervised Uncertainty Estimation For Super-Resolution of Satellite Images
Zhe Zheng, Valéry Dewil, Pablo Arias
Super-resolution (SR) of satellite imagery is challenging due to the lack of paired low-/high-resolution data. Recent self-supervised SR methods overcome this limitation by exploit…
Inversion of Magnetic Data using Learned Dictionaries and Scale Space
Shadab Ahamed, Simon Ghyselincks, Pablo Chang Huang Arias +5
Magnetic data inversion is an important tool in geophysics, used to infer subsurface magnetic susceptibility distributions from surface magnetic field measurements. This inverse pr…
SING: A Plug-and-Play DNN Learning Technique
Adrien Courtois, Damien Scieur, Jean-Michel Morel +2
We propose SING (StabIlized and Normalized Gradient), a plug-and-play technique that improves the stability and generalization of the Adam(W) optimizer. SING is straightforward to…
L1BSR: Exploiting Detector Overlap for Self-Supervised Single-Image Super-Resolution of Sentinel-2 L1B Imagery
Ngoc Long Nguyen, Jérémy Anger, Axel Davy +2
High-resolution satellite imagery is a key element for many Earth monitoring applications. Satellites such as Sentinel-2 feature characteristics that are favorable for super-resolu…
Can neural networks extrapolate? Discussion of a theorem by Pedro Domingos
Adrien Courtois, Jean-Michel Morel, Pablo Arias
Neural networks trained on large datasets by minimizing a loss have become the state-of-the-art approach for resolving data science problems, particularly in computer vision, image…
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…
Self-supervision versus synthetic datasets: which is the lesser evil in the context of video denoising?
Valéry Dewil, Aranud Barral, Gabriele Facciolo +1
Supervised training has led to state-of-the-art results in image and video denoising. However, its application to real data is limited since it requires large datasets of noisy-cle…
Investigating Neural Architectures by Synthetic Dataset Design
Adrien Courtois, Jean-Michel Morel, Pablo Arias
Recent years have seen the emergence of many new neural network structures (architectures and layers). To solve a given task, a network requires a certain set of abilities reflecte…
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…
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…
Implementation of the VBM3D Video Denoising Method and Some Variants
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 doma…
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…
Model-blind Video Denoising Via Frame-to-frame Training
Thibaud Ehret, Axel Davy, Jean-Michel Morel +2
Modeling the processing chain that has produced a video is a difficult reverse engineering task, even when the camera is available. This makes model based video processing a still…
Non-Local Video Denoising by CNN
Axel Davy, Thibaud Ehret, Jean-Michel Morel +2
Non-local patch based methods were until recently state-of-the-art for image denoising but are now outperformed by CNNs. Yet they are still the state-of-the-art for video denoising…