BRIE , David

Hyperspectral image (HSI) deconvolution is an ill-posed problem aiming at recovering sharp images with tens or hundreds of spectral channels from blurred and noisy observations. In order to successfully conduct the deconvolution, proper priors are required to regularize the optimization problem. However, handcrafting a good regularizer may not...

Zero-attracting least-mean-square (ZA-LMS) algorithm has been widely used for online sparse system identification. It combines the LMS framework and 1-norm regularization to promote sparsity, and relies on subgradient iterations. Despite the significant interest in ZA-LMS, few works analyzed its transient behavior. The main difficulty lies in...

This paper combines supervised linear unmixing and deconvolution problems to increase the resolution of the abundance maps for industrial imaging systems. The joint unmixing-deconvolution (JUD) algorithm is introduced based on the Tikhonov regularization criterion for offline processing. In order to meet the needs of industrial applications,...

This paper proposes a joint supervised linear unmixing and deconvolution algorithm (JUD) to increase the resolution of the abundance maps for industrial imaging systems. The JUD algorithm is introduced based on a Tikhonov regularization criterion for offline processing. In order to meet the needs of industrial applications, the proposed JUD is...

This paper introduces a framework based on the LMS algorithm for sequential deconvolution of hyperspectral images acquired by industrial pushbroom imaging systems. Considering a sequential model of image blurring phenomenon, we derive a sliding-block zero-attracting LMS algorithm with spectral regularization. The role of each hyper-parameter is...

Hyperspectral imaging has received considerable attention in the last decade as it combines the power of digital imaging and spectroscopy. Every pixel in a hyperspectral image provides local spectral information about a scene of interest across a large number of contiguous bands. Several sensing techniques have been devised for hyperspectral...

Cet article s'intéresse à la conception d'une méthode séquentielle de déconvolution d'images hyperspectrales acquises par un imageur pushbroom. À partir de l'écriture sous forme séquentielle de l'image floutée, on propose un algorithme de type LMS (least mean squares) par bloc glissant qui inclut des termes de régularisation spatiale et...

This paper proposes a hyperspectral image deconvolution algorithm for the online restoration of hyperspectral images as provided by wiskbroom and pushbroom scanning systems. We introduce a least-mean-squares (LMS)-based framework accounting for the convolution kernel non-causality and including non-quadratic (zero attracting and piece-wise...

This paper proposes a hyperspectral image deconvolution algorithm for the online restoration of hyperspectral images as provided by wiskbroom and pushbroom scanning systems. We introduce a least-mean-squares (LMS)-based framework accounting for the convolution kernel non-causality and including non-quadratic (zero attracting and piece-wise...

Coupled tensor approximation has recently emerged as a promising approach for the fusion of hyperspectral and multispectral images, reconciling state of the art performance with strong theoretical guarantees. However, tensor-based approaches previously proposed assume that the different observed images are acquired under exactly the same...

Nous proposons une solution conjointe aux problèmes de super-résolution et de démélange de l'image super-résolue. Cette approche utilise la décomposition tensorielle LL1 et tient compte d'un phénomène de variabilité spectrale. Des garanties théoriques de reconstruction sont fournies. Nous proposons un algorithme sous contraintes de positivité,...

Coupled tensor approximation has recently emerged as a promising approach for the fusion of hyperspectral and multispectral images (respectively HSI and MSI). This problem is referred to as hyperspectral super-resolution, and consists in recovering a super-resolution image (SRI). Previously proposed tensor-based approaches share a common...

In this paper, we propose to jointly solve the hyperspectral super-resolution problem and the unmixing problem of the underlying super-resolution image using a coupled LL1 block-tensor decomposition. We consider a spectral variability phenomenon occurring between the observed low-resolution images. Exact recovery conditions for the image and...

In this paper we propose a hyperspectral and multispectral image fusion framework accounting for inter-image variability. The images are represented as three dimensional tensors, and both the high-resolution image and inter-image variations are assumed to admit a Tucker decomposition. Two algorithms are proposed, one purely algebraic and...

Over the last two decades, tensor-based methods have received growing attention in the signal processing community. In this work, we propose a comprehensive overview of tensor-based models and methods for multisensor signal processing. We present for instance the Tucker decomposition, the Canonical Polyadic Decomposition (CPD), the Tensor-Train...