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 • 27 Dec 2022 • Bruno Galerne, Lara Raad, José Lezama, Jean-Michel Morel
Neural style transfer is a deep learning technique that produces an unprecedentedly rich style transfer from a style image to a content image and is particularly impressive when it comes to transferring style from a painting to an image.
no code implementations • 3 Nov 2022 • Marcelo Sanchez, Gil Triginer, Coloma Ballester, Lara Raad, Eduard Ramon
In this work, we revisit a two-stage approach for retouching facial wrinkles and obtain results with unprecedented realism.
no code implementations • 6 Apr 2022 • Coloma Ballester, Aurélie Bugeau, Hernan Carrillo, Michaël Clément, Rémi Giraud, Lara Raad, Patricia Vitoria
While learning to automatically colorize an image, one can define well-suited objective functions related to the desired color output.
no code implementations • 6 Apr 2022 • Coloma Ballester, Aurélie Bugeau, Hernan Carrillo, Michaël Clément, Rémi Giraud, Lara Raad, Patricia Vitoria
In this chapter, we aim to study their influence on the results obtained by training a deep neural network, to answer the question: "Is it crucial to correctly choose the right color space in deep-learning based colorization?".
3 code implementations • 23 Jul 2019 • Patricia Vitoria, Lara Raad, Coloma Ballester
In this paper, we propose an adversarial learning colorization approach coupled with semantic information.
no code implementations • 22 Jul 2017 • Lara Raad, Axel Davy, Agnès Desolneux, Jean-Michel Morel
The two main approaches are statistics-based methods and patch re-arrangement methods.