Search Results for author: Jean-Yves Tourneret

Found 23 papers, 2 papers with code

A Robust and Flexible EM Algorithm for Mixtures of Elliptical Distributions with Missing Data

1 code implementation28 Jan 2022 Florian Mouret, Alexandre Hippert-Ferrer, Frédéric Pascal, Jean-Yves Tourneret

To overcome this issue, a new EM algorithm is investigated for mixtures of elliptical distributions with the property of handling potential missing data.

Imputation

Outlier detection at the parcel-level in wheat and rapeseed crops using multispectral and SAR time series

no code implementations17 Apr 2020 Florian Mouret, Mohanad Albughdadi, Sylvie Duthoit, Denis Kouamé, Guillaume Rieu, Jean-Yves Tourneret

When using these features with an outlier ratio of 10%, the percentage of detected true positives (i. e., crop anomalies) is equal to 94. 1% for rapeseed parcels and 95. 5% for wheat parcels.

Anomaly Detection Outlier Detection +1

Seeing Around Corners with Edge-Resolved Transient Imaging

no code implementations17 Feb 2020 Joshua Rapp, Charles Saunders, Julián Tachella, John Murray-Bruce, Yoann Altmann, Jean-Yves Tourneret, Stephen McLaughlin, Robin M. A. Dawson, Franco N. C. Wong, Vivek K Goyal

Non-line-of-sight (NLOS) imaging is a rapidly growing field seeking to form images of objects outside the field of view, with potential applications in search and rescue, reconnaissance, and even medical imaging.

Spectral Variability in Hyperspectral Data Unmixing: A Comprehensive Review

no code implementations21 Jan 2020 Ricardo Augusto Borsoi, Tales Imbiriba, José Carlos Moreira Bermudez, Cédric Richard, Jocelyn Chanussot, Lucas. Drumetz, Jean-Yves Tourneret, Alina Zare, Christian Jutten

The spectral signatures of the materials contained in hyperspectral images, also called endmembers (EM), can be significantly affected by variations in atmospheric, illumination or environmental conditions typically occurring within an image.

A Tensor Factorization Method for 3D Super-Resolution with Application to Dental CT

1 code implementation26 Jul 2018 Janka Hatvani, Adrian Basarab, Jean-Yves Tourneret, Miklós Gyöngy, Denis Kouamé

In this article this factorization framework is investigated for single image resolution enhancement with an off-line estimate of the system point spread function.

Super-Resolution

A Fast Algorithm Based on a Sylvester-like Equation for LS Regression with GMRF Prior

no code implementations18 Sep 2017 Qi Wei, Emilie Chouzenoux, Jean-Yves Tourneret, Jean-Christophe Pesquet

This paper presents a fast approach for penalized least squares (LS) regression problems using a 2D Gaussian Markov random field (GMRF) prior.

regression

An unsupervised bayesian approach for the joint reconstruction and classification of cutaneous reflectance confocal microscopy images

no code implementations4 Mar 2017 Abdelghafour Halimi, Hadj Batatia, Jimmy Le Digabel, Gwendal Josse, Jean-Yves Tourneret

This paper studies a new Bayesian algorithm for the joint reconstruction and classification of reflectance confocal microscopy (RCM) images, with application to the identification of human skin lentigo.

General Classification

Unsupervised Nonlinear Spectral Unmixing based on a Multilinear Mixing Model

no code implementations14 Apr 2016 Qi Wei, Marcus Chen, Jean-Yves Tourneret, Simon Godsill

In the community of remote sensing, nonlinear mixing models have recently received particular attention in hyperspectral image processing.

R-FUSE: Robust Fast Fusion of Multi-Band Images Based on Solving a Sylvester Equation

no code implementations6 Apr 2016 Qi Wei, Nicolas Dobigeon, Jean-Yves Tourneret, Jose Bioucas-Dias, Simon Godsill

This paper proposes a robust fast multi-band image fusion method to merge a high-spatial low-spectral resolution image and a low-spatial high-spectral resolution image.

Multi-Band Image Fusion Based on Spectral Unmixing

no code implementations29 Mar 2016 Qi Wei, Jose Bioucas-Dias, Nicolas Dobigeon, Jean-Yves Tourneret, Marcus Chen, Simon Godsill

The non-negativity and sum-to-one constraints resulting from the intrinsic physical properties of the abundances are introduced as prior information to regularize this ill-posed problem.

Online Unmixing of Multitemporal Hyperspectral Images accounting for Spectral Variability

no code implementations20 Oct 2015 Pierre-Antoine Thouvenin, Nicolas Dobigeon, Jean-Yves Tourneret

Hyperspectral unmixing is aimed at identifying the reference spectral signatures composing an hyperspectral image and their relative abundance fractions in each pixel.

Hyperspectral Unmixing

Fast Single Image Super-Resolution

no code implementations1 Oct 2015 Ningning Zhao, Qi Wei, Adrian Basarab, Nicolas Dobigeon, Denis Kouame, Jean-Yves Tourneret

Specifically, an analytical solution can be obtained and implemented efficiently for the Gaussian prior or any other regularization that can be formulated into an $\ell_2$-regularized quadratic model, i. e., an $\ell_2$-$\ell_2$ optimization problem.

Image Super-Resolution Single Image Super Resolution

Fast Spectral Unmixing based on Dykstra's Alternating Projection

no code implementations7 May 2015 Qi Wei, Jose Bioucas-Dias, Nicolas Dobigeon, Jean-Yves Tourneret

This paper presents a fast spectral unmixing algorithm based on Dykstra's alternating projection.

Hyperspectral pansharpening: a review

no code implementations17 Apr 2015 Laetitia Loncan, Luis B. Almeida, José M. Bioucas-Dias, Xavier Briottet, Jocelyn Chanussot, Nicolas Dobigeon, Sophie Fabre, Wenzhi Liao, Giorgio A. Licciardi, Miguel Simões, Jean-Yves Tourneret, Miguel A. Veganzones, Gemine Vivone, Qi Wei, Naoto Yokoya

In this work, we compare new pansharpening techniques designed for hyperspectral data with some of the state of the art methods for multispectral pansharpening, which have been adapted for hyperspectral data.

Pansharpening

Nonparametric Detection of Nonlinearly Mixed Pixels and Endmember Estimation in Hyperspectral Images

no code implementations18 Mar 2015 Tales Imbiriba, José Carlos Moreira Bermudez, Cédric Richard, Jean-Yves Tourneret

The detection approach is based on the comparison of the reconstruction errors using both a Gaussian process regression model and a linear regression model.

Hyperspectral Unmixing regression

Fast Fusion of Multi-Band Images Based on Solving a Sylvester Equation

no code implementations10 Feb 2015 Qi Wei, Nicolas Dobigeon, Jean-Yves Tourneret

This paper proposes a fast multi-band image fusion algorithm, which combines a high-spatial low-spectral resolution image and a low-spatial high-spectral resolution image.

Bayesian estimation of the multifractality parameter for image texture using a Whittle approximation

no code implementations17 Oct 2014 Sébastien Combrexelle, Herwig Wendt, Nicolas Dobigeon, Jean-Yves Tourneret, Steve McLaughlin, Patrice Abry

Multifractal analysis is a useful signal and image processing tool, yet, the accurate estimation of multifractal parameters for image texture remains a challenge.

Hyperspectral and Multispectral Image Fusion based on a Sparse Representation

no code implementations19 Sep 2014 Qi Wei, José Bioucas-Dias, Nicolas Dobigeon, Jean-Yves Tourneret

This paper presents a variational based approach to fusing hyperspectral and multispectral images.

Joint Bayesian estimation of close subspaces from noisy measurements

no code implementations1 Oct 2013 Olivier Besson, Nicolas Dobigeon, Jean-Yves Tourneret

In this letter, we consider two sets of observations defined as subspace signals embedded in noise and we wish to analyze the distance between these two subspaces.

Bayesian Fusion of Multi-Band Images

no code implementations23 Jul 2013 Qi Wei, Nicolas Dobigeon, Jean-Yves Tourneret

In this paper, a Bayesian fusion technique for remotely sensed multi-band images is presented.

Nonlinear unmixing of hyperspectral images: models and algorithms

no code implementations6 Apr 2013 Nicolas Dobigeon, Jean-Yves Tourneret, Cédric Richard, José C. M. Bermudez, Stephen McLaughlin, Alfred O. Hero

When considering the problem of unmixing hyperspectral images, most of the literature in the geoscience and image processing areas relies on the widely used linear mixing model (LMM).

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