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 • 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 • 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 • 3 Feb 2021 • Jérémy Anger, Thibaud Ehret, Gabriele Facciolo
Recent constellations of satellites, including the Skysat constellation, are able to acquire bursts of images.
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 • 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.
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.
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.
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.