5 papers
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…
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…
Hölder continuity and composition operators in pluriharmonic Bloch spaces
Jie Huang, Suman Das, Antti Rasila
We show a Hölder estimate of order for pluriharmonic Bloch mappings in the unit ball with respect to the Bergman metric. We apply this to…
Processsing Simple Geometric Attributes with Autoencoders
Alasdair Newson, Andrés Almansa, Yann Gousseau +1
Image synthesis is a core problem in modern deep learning, and many recent architectures such as autoencoders and Generative Adversarial networks produce spectacular results on hig…