6 citations · 11 across the 9 of their papers we have counts for
10 papers
Benchmarking Hyperspectral Foundation Models for Hyperspectral Unmixing
Edgard Dabier, Christophe Kervazo, Pietro Gori +1
Several foundation models dedicated to hyperspectral images have recently been made available. These models are trained on large unlabeled datasets and exhibit strong performance o…
SCALMU: Synthetically-trained Coupling of Adaptive Learned Multiplicative Updates for Hyperspectral-Multispectral Fusion
Xinxin Xu, Yann Gousseau, Christophe Kervazo +1
HyperSpectral-MultiSpectral Image (HSI-MSI) fusion aims to recover a high-resolution hyperspectral image from a low-resolution HSI and a high-resolution MSI. Classical methods such…
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…
Uncertainty quantification for fast reconstruction methods using augmented equivariant bootstrap: Application to radio interferometry
Mostafa Cherif, Tobías I. Liaudat, Jonathan Kern +2
The advent of next-generation radio interferometers like the Square Kilometer Array promises to revolutionise our radio astronomy observational capabilities. The unprecedented volu…
Unrolling PALM for sparse semi-blind source separation
Mohammad Fahes, Christophe Kervazo, Jérôme Bobin +1
Sparse Blind Source Separation (BSS) has become a well established tool for a wide range of applications - for instance, in astrophysics and remote sensing. Classical sparse BSS me…
Smoothed Separable Nonnegative Matrix Factorization
Nicolas Nadisic, Nicolas Gillis, Christophe Kervazo
Given a set of data points belonging to the convex hull of a set of vertices, a key problem in linear algebra, signal processing, data analysis and machine learning is to estimate…
Successive Nonnegative Projection Algorithm for Linear Quadratic Mixtures
Christophe Kervazo, Nicolas Gillis, Nicolas Dobigeon
In this work, we tackle the problem of hyperspectral (HS) unmixing by departing from the usual linear model and focusing on a Linear-Quadratic (LQ) one. The proposed algorithm, ref…
Provably robust blind source separation of linear-quadratic near-separable mixtures
Christophe Kervazo, Nicolas Gillis, Nicolas Dobigeon
In this work, we consider the problem of blind source separation (BSS) by departing from the usual linear model and focusing on the linear-quadratic (LQ) model. We propose two prov…
Heuristics for Efficient Sparse Blind Source Separation
Christophe Kervazo, Jerome Bobin, Cecile Chenot
Sparse Blind Source Separation (sparse BSS) is a key method to analyze multichannel data in fields ranging from medical imaging to astrophysics. However, since it relies on seeking…