Search Results for author:
Found 44 papers, 5 papers with code
RFI-DRUnet: Restoring dynamic spectra corrupted by radio frequency interference -- Application to pulsar observations
Conversely, this work proposes to tackle RFI mitigation as a joint detection and restoration that allows parts of the dynamic spectrum affected by RFI to be not only identified but also recovered.
Regularization by denoising: Bayesian model and Langevin-within-split Gibbs sampling
This paper introduces a Bayesian framework for image inversion by deriving a probabilistic counterpart to the regularization-by-denoising (RED) paradigm.
AE-RED: A Hyperspectral Unmixing Framework Powered by Deep Autoencoder and Regularization by Denoising
In this way, both the characteristics of the deep autoencoder based unmixing methods and priors provided by denoisers are merged into our well-designed framework to enhance the unmixing performance.
Guided Deep Generative Model-based Spatial Regularization for Multiband Imaging Inverse Problems
More precisely, the regularization is conceived as a deep generative network able to encode spatial semantic features contained in this auxiliary image of high spatial resolution.
Normalizing flow sampling with Langevin dynamics in the latent space
Normalizing flows (NF) use a continuous generator to map a simple latent (e. g. Gaussian) distribution, towards an empirical target distribution associated with a training data set.
Plug-and-Play split Gibbs sampler: embedding deep generative priors in Bayesian inference
This paper introduces a stochastic plug-and-play (PnP) sampling algorithm that leverages variable splitting to efficiently sample from a posterior distribution.
Probabilistic Simplex Component Analysis by Importance Sampling
In this paper we consider the problem of linear unmixing hidden random variables defined over the simplex with additive Gaussian noise, also known as probabilistic simplex component analysis (PRISM).
Sliced-Wasserstein normalizing flows: beyond maximum likelihood training
Despite their advantages, normalizing flows generally suffer from several shortcomings including their tendency to generate unrealistic data (e. g., images) and their failing to detect out-of-distribution data.
Learning Optimal Transport Between two Empirical Distributions with Normalizing Flows
Each of these functions is associated to one sub-flow of the network, whose output provides intermediate steps of the transport between the original and target measures.
CD-GAN: a robust fusion-based generative adversarial network for unsupervised remote sensing change detection with heterogeneous sensors
In the context of Earth observation, change detection boils down to comparing images acquired at different times by sensors of possibly different spatial and/or spectral resolutions or different modalities (e. g., optical or radar).
Successive Nonnegative Projection Algorithm for Linear Quadratic Mixtures
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.
Provably robust blind source separation of linear-quadratic near-separable mixtures
The BF is in turn shown to be robust to noise under easier-to-check and milder conditions than SNPALQ.
High-dimensional Gaussian sampling: a review and a unifying approach based on a stochastic proximal point algorithm
In addition to this state of the art, this paper proposes a unifying Gaussian simulation framework by deriving a stochastic counterpart of the celebrated proximal point algorithm in optimization.
Computation
Fast reconstruction of atomic-scale STEM-EELS images from sparse sampling
Besides, among the methods proposed in the microscopy literature, some are fast but inaccurate while others provide accurate reconstruction but at the price of a high computation burden.
Hyperspectral and multispectral image fusion under spectrally varying spatial blurs -- Application to high dimensional infrared astronomical imaging
Hyperspectral imaging has become a significant source of valuable data for astronomers over the past decades.
Matrix cofactorization for joint spatial-spectral unmixing of hyperspectral images
The most common way to introduce spatial information is to rely on a spatial regularization of the abundance maps.
Asymptotically exact data augmentation: models, properties and algorithms
In a broader perspective, this paper shows that AXDA models can benefit from interesting statistical properties and yield efficient inference algorithms.
Matrix Cofactorization for Joint Representation Learning and Supervised Classification -- Application to Hyperspectral Image Analysis
The attribution vectors of the clustering are then used as features vectors for the classification task, i. e., the coding vectors of the corresponding factorization problem.
Factor analysis of dynamic PET images: beyond Gaussian noise
Factor analysis has proven to be a relevant tool for extracting tissue time-activity curves (TACs) in dynamic PET images, since it allows for an unsupervised analysis of the data.
Coupled dictionary learning for unsupervised change detection between multi-sensor remote sensing images
This paper addresses the problem of unsupervisedly detecting changes between two observed images acquired by sensors of different modalities with possibly different resolutions.
Hyperspectral unmixing with spectral variability using adaptive bundles and double sparsity
The proposed method is designed to promote sparsity on the selection of both spectra and classes within each pixel.
Robust fusion algorithms for unsupervised change detection between multi-band optical images - A comprehensive case study
Unsupervised change detection techniques are generally constrained to two multi-band optical images acquired at different times through sensors sharing the same spatial and spectral resolution.
Reconstruction of partially sampled multi-band images - Application to STEM-EELS imaging
Electron microscopy has shown to be a very powerful tool to map the chemical nature of samples at various scales down to atomic resolution.
Hierarchical Bayesian image analysis: from low-level modeling to robust supervised learning
Within a supervised classification framework, labeled data are used to learn classifier parameters.
Bayesian nonparametric Principal Component Analysis
The selection of the number of significant components is essential but often based on some practical heuristics depending on the application.
Unmixing dynamic PET images with variable specific binding kinetics
Modeling the variability of the specific binding factor has a strong potential impact for dynamic PET image analysis.
Detecting Changes Between Optical Images of Different Spatial and Spectral Resolutions: a Fusion-Based Approach
Change detection is one of the most challenging issues when analyzing remotely sensed images.
Robust Fusion of Multi-Band Images with Different Spatial and Spectral Resolutions for Change Detection
To alleviate these issues, classical change detection methods are applied after independent preprocessing steps (e. g., resampling) used to get the same spatial and spectral resolutions for the pair of observed images.
Bayesian selection for the l2-Potts model regularization parameter: 1D piecewise constant signal denoising
Piecewise constant denoising can be solved either by deterministic optimization approaches, based on the Potts model, or by stochastic Bayesian procedures.
R-FUSE: Robust Fast Fusion of Multi-Band Images Based on Solving a Sylvester Equation
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
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.
Bayesian anti-sparse coding
Then this probability distribution is used as a prior to promote anti-sparsity in a Gaussian linear inverse problem, yielding a fully Bayesian formulation of anti-sparse coding.
Online Unmixing of Multitemporal Hyperspectral Images accounting for Spectral Variability
Hyperspectral unmixing is aimed at identifying the reference spectral signatures composing an hyperspectral image and their relative abundance fractions in each pixel.
Fast Single Image Super-Resolution
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.
Fast Spectral Unmixing based on Dykstra's Alternating Projection
This paper presents a fast spectral unmixing algorithm based on Dykstra's alternating projection.
Combining local regularity estimation and total variation optimization for scale-free texture segmentation
Texture segmentation constitutes a standard image processing task, crucial to many applications.
Hyperspectral pansharpening: a review
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.
Fast Fusion of Multi-Band Images Based on Solving a Sylvester Equation
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
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
This paper presents a variational based approach to fusing hyperspectral and multispectral images.
Nonlinear hyperspectral unmixing with robust nonnegative matrix factorization
This paper introduces a robust mixing model to describe hyperspectral data resulting from the mixture of several pure spectral signatures.
Joint Bayesian estimation of close subspaces from noisy measurements
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
In this paper, a Bayesian fusion technique for remotely sensed multi-band images is presented.
Nonlinear unmixing of hyperspectral images: models and algorithms
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).
