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20082025
most citedHyperspectral and Multispectral Image Fusion based on a Sparse Representation

709 citations · 4.7k across the 56 of their papers we have counts for

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Showing 2014Show all

6 papers · 1 filter

cs.CV2014★ 3 cited

Joint Segmentation and Deconvolution of Ultrasound Images Using a Hierarchical Bayesian Model based on Generalized Gaussian Priors

Ningning Zhao, Adrian Basarab, Denis Kouame +1

This paper proposes a joint segmentation and deconvolution Bayesian method for medical ultrasound (US) images. Contrary to piecewise homogeneous images, US images exhibit heavy cha…

math.NA2014

Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)

L. Jacques, C. De Vleeschouwer, Y. Boursier +64

The implicit objective of the biennial "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) is to foster collaboration between interna…

physics.data-an2014★ 23 cited

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

Sébastien Combrexelle, Herwig Wendt, Nicolas Dobigeon +3

Texture characterization is a central element in many image processing applications. Multifractal analysis is a useful signal and image processing tool, yet, the accurate estimatio…

cs.CV2014★ 709 cited

Hyperspectral and Multispectral Image Fusion based on a Sparse Representation

Qi Wei, José Bioucas-Dias, Nicolas Dobigeon +1

This paper presents a variational based approach to fusing hyperspectral and multispectral images. The fusion process is formulated as an inverse problem whose solution is the targ…

stat.ME2014★ 78 cited

Unsupervised Unmixing of Hyperspectral Images Accounting for Endmember Variability

Abderrahim Halimi, Nicolas Dobigeon, Jean-Yves Tourneret

This paper presents an unsupervised Bayesian algorithm for hyperspectral image unmixing accounting for endmember variability. The pixels are modeled by a linear combination of endm…

stat.ME2014

A Hamiltonian Monte Carlo Method for Non-Smooth Energy Sampling

Lotfi Chaari, Jean-Yves Tourneret, Caroline Chaux +1

Efficient sampling from high-dimensional distributions is a challenging issue which is encountered in many large data recovery problems involving Markov chain Monte Carlo schemes.…