4 papers
Synthetic Abundance Maps for Unsupervised Super-Resolution of Hyperspectral Remote Sensing Images
Xinxin Xu, Yann Gousseau, Christophe Kervazo +1
Hyperspectral single image super-resolution (HS-SISR) aims to enhance the spatial resolution of hyperspectral images to fully exploit their spectral information. While considerable…
Super-résolution non supervisée d'images hyperspectrales de télédétection utilisant un entraînement entièrement synthétique
Xinxin Xu, Yann Gousseau, Christophe Kervazo +1
Hyperspectral single image super-resolution (SISR) aims to enhance spatial resolution while preserving the rich spectral information of hyperspectral images. Most existing methods…
Unsupervised Super-Resolution of Hyperspectral Remote Sensing Images Using Fully Synthetic Training
Xinxin Xu, Yann Gousseau, Christophe Kervazo +1
Considerable work has been dedicated to hyperspectral single image super-resolution to improve the spatial resolution of hyperspectral images and fully exploit their potential. How…
VibrantLeaves: A principled parametric image generator for training deep restoration models
Raphael Achddou, Yann Gousseau, Saïd Ladjal +3
In this paper, we introduce a synthetic image generator relying on a few simple principles, specifically focusing on geometric modeling, textures, and a simple modeling of image ac…