Search Results for author: Cédric Richard

Found 34 papers, 6 papers with code

Spatial Deep Deconvolution U-Net for Traffic Analyses with Distributed Acoustic Sensing

no code implementations7 Dec 2022 Siyuan Yuan, Martijn van den Ende, Jingxiao Liu, Hae Young Noh, Robert Clapp, Cédric Richard, Biondo Biondi

In response, we introduce a self-supervised U-Net model that can suppress background noise and compress car-induced DAS signals into high-resolution pulses through spatial deconvolution.

Tuning-free Plug-and-Play Hyperspectral Image Deconvolution with Deep Priors

1 code implementation28 Nov 2022 Xiuheng Wang, Jie Chen, Cédric Richard

Deconvolution is a widely used strategy to mitigate the blurring and noisy degradation of hyperspectral images~(HSI) generated by the acquisition devices.

Denoising Image Deconvolution

Deep Hyperspectral and Multispectral Image Fusion with Inter-image Variability

1 code implementation24 Aug 2022 Xiuheng Wang, Ricardo Augusto Borsoi, Cédric Richard, Jie Chen

The fusion problem is stated as an optimization problem in the maximum a posteriori framework.

Adaptive Random Fourier Features Kernel LMS

no code implementations14 Jul 2022 Wei Gao, Jie Chen, Cédric Richard, Wentao Shi, Qunfei Zhang

We propose the adaptive random Fourier features Gaussian kernel LMS (ARFF-GKLMS).

Integration of Physics-Based and Data-Driven Models for Hyperspectral Image Unmixing

1 code implementation11 Jun 2022 Jie Chen, Min Zhao, Xiuheng Wang, Cédric Richard, Susanto Rahardja

Spectral unmixing is one of the most important quantitative analysis tasks in hyperspectral data processing.

Hyperspectral Unmixing

Hyperspectral Image Super-resolution with Deep Priors and Degradation Model Inversion

1 code implementation24 Jan 2022 Xiuheng Wang, Jie Chen, Cédric Richard

To overcome inherent hardware limitations of hyperspectral imaging systems with respect to their spatial resolution, fusion-based hyperspectral image (HSI) super-resolution is attracting increasing attention.

Hyperspectral Image Super-Resolution Image Super-Resolution

Transient Performance Analysis of the $\ell_1$-RLS

no code implementations14 Sep 2021 Wei Gao, Jie Chen, Cédric Richard, Wentao Shi, Qunfei Zhang

The recursive least-squares algorithm with $\ell_1$-norm regularization ($\ell_1$-RLS) exhibits excellent performance in terms of convergence rate and steady-state error in identification of sparse systems.

Graph topology inference with derivative-reproducing property in RKHS: algorithm and convergence analysis

no code implementations28 Apr 2021 Mircea Moscu, Ricardo A. Borsoi, Cédric Richard, José-Carlos M. Bermudez

Contrasting with previous approaches based on linear models, the considered model is able to explain nonlinear interactions between the agents in a network.

Fast Unmixing and Change Detection in Multitemporal Hyperspectral Data

no code implementations7 Apr 2021 Ricardo Augusto Borsoi, Tales Imbiriba, José Carlos Moreira Bermudez, Cédric Richard

However, MESMA does not consider the relationship between the different HIs, and its computational complexity is extremely high for large spectral libraries.

Change Detection

Transient Theoretical Analysis of Diffusion RLS Algorithm for Cyclostationary Colored Inputs

no code implementations12 Jan 2021 Wei Gao, Jie Chen, Cédric Richard

Convergence of the diffusion RLS (DRLS) algorithm to steady-state has been extensively studied in the literature, whereas no analysis of its transient convergence behavior has been reported yet.

Hyperspectral Image Super-Resolution via Deep Prior Regularization with Parameter Estimation

1 code implementation9 Sep 2020 Xiuheng Wang, Jie Chen, Qi Wei, Cédric Richard

Furthermore, the regularization parameter is simultaneously estimated to automatically adjust contribution of the physical model and {the} learned prior to reconstruct the final HR HSI.

Hyperspectral Image Super-Resolution Image Super-Resolution

Online Graph-Based Change Point Detection in Multiband Image Sequences

no code implementations24 Jun 2020 Ricardo Augusto Borsoi, Cédric Richard, André Ferrari, Jie Chen, José Carlos Moreira Bermudez

To effectively perform change-point detection in multitemporal images, it is important to devise techniques that are computationally efficient for processing large datasets, and that do not require knowledge about the nature of the changes.

Change Point Detection

Online change-point detection with kernels

no code implementations7 Feb 2020 André Ferrari, Cédric Richard, Anthony Bourrier, Ikram Bouchikhi

Change-points in time series data are usually defined as the time instants at which changes in their properties occur.

Change Point Detection Time Series +1

Spectral Variability in Hyperspectral Data Unmixing: A Comprehensive Review

1 code implementation21 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.

Kalman Filtering and Expectation Maximization for Multitemporal Spectral Unmixing

no code implementations2 Jan 2020 Ricardo Augusto Borsoi, Tales Imbiriba, Pau Closas, José Carlos Moreira Bermudez, Cédric Richard

The recent evolution of hyperspectral imaging technology and the proliferation of new emerging applications presses for the processing of multiple temporal hyperspectral images.

Deep Generative Models for Library Augmentation in Multiple Endmember Spectral Mixture Analysis

no code implementations20 Sep 2019 Ricardo Augusto Borsoi, Tales Imbiriba, José Carlos Moreira Bermudez, Cédric Richard

Multiple Endmember Spectral Mixture Analysis (MESMA) is one of the leading approaches to perform spectral unmixing (SU) considering variability of the endmembers (EMs).

A Blind Multiscale Spatial Regularization Framework for Kernel-based Spectral Unmixing

no code implementations19 Aug 2019 Ricardo Augusto Borsoi, Tales Imbiriba, José Carlos Moreira Bermudez, Cédric Richard

Furthermore, we employ a theory-based statistical framework to devise a consistent strategy to estimate all required parameters, including both the regularization parameters of the algorithm and the number of superpixels of the transformation, resulting in a truly blind (from the parameters setting perspective) unmixing method.

Denoising Superpixels

Tech Report: A Fast Multiscale Spatial Regularization for Sparse Hyperspectral Unmixing

no code implementations5 Dec 2017 Ricardo Augusto Borsoi, Tales Imbiriba, José Carlos Moreira Bermudez, Cédric Richard

Sparse hyperspectral unmixing from large spectral libraries has been considered to circumvent limitations of endmember extraction algorithms in many applications.

Hyperspectral Unmixing Superpixels

On reducing the communication cost of the diffusion LMS algorithm

no code implementations30 Nov 2017 Ibrahim El Khalil Harrane, Rémi Flamary, Cédric Richard

While these data may be processed in a centralized manner, it is often more suitable to consider distributed strategies such as diffusion as they are scalable and can handle large amounts of data by distributing tasks over networked agents.

Adaptation and learning over networks for nonlinear system modeling

no code implementations28 Apr 2017 Simone Scardapane, Jie Chen, Cédric Richard

In this chapter, we analyze nonlinear filtering problems in distributed environments, e. g., sensor networks or peer-to-peer protocols.

Multitask diffusion adaptation over networks with common latent representations

no code implementations13 Feb 2017 Jie Chen, Cédric Richard, Ali H. Sayed

Online learning with streaming data in a distributed and collaborative manner can be useful in a wide range of applications.

Technical Report: Band selection for nonlinear unmixing of hyperspectral images as a maximal clique problem

no code implementations1 Mar 2016 Tales Imbiriba, José Carlos Moreira Bermudez, Cédric Richard

Kernel-based nonlinear mixing models have been applied to unmix spectral information of hyperspectral images when the type of mixing occurring in the scene is too complex or unknown.

Stochastic Behavior of the Nonnegative Least Mean Fourth Algorithm for Stationary Gaussian Inputs and Slow Learning

no code implementations24 Aug 2015 Jingen Ni, Jian Yang, Jie Chen, Cédric Richard, José Carlos M. Bermudez

Some system identification problems impose nonnegativity constraints on the parameters to estimate due to inherent physical characteristics of the unknown system.

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

A graph Laplacian regularization for hyperspectral data unmixing

no code implementations14 Oct 2014 Rita Ammanouil, André Ferrari, Cédric Richard

This paper introduces a graph Laplacian regularization in the hyperspectral unmixing formulation.

Hyperspectral Unmixing

Blind and fully constrained unmixing of hyperspectral images

no code implementations3 Mar 2014 Rita Ammanouil, André Ferrari, Cédric Richard, David Mary

This paper addresses the problem of blind and fully constrained unmixing of hyperspectral images.

Steady-state performance of non-negative least-mean-square algorithm and its variants

no code implementations24 Jan 2014 Jie Chen, José Carlos M. Bermudez, Cédric Richard

The transient behavior of the NNLMS, Normalized NNLMS, Exponential NNLMS and Sign-Sign NNLMS algorithms have been studied in our previous work.

Convergence analysis of kernel LMS algorithm with pre-tuned dictionary

no code implementations31 Oct 2013 Jie Chen, Wei Gao, Cédric Richard, Jose-Carlos M. Bermudez

In addition to choosing a reproducing kernel and setting filter parameters, designing a KLMS adaptive filter requires to select a so-called dictionary in order to get a finite-order model.

Nonlinear unmixing of hyperspectral images using a semiparametric model and spatial regularization

no code implementations31 Oct 2013 Jie Chen, Cédric Richard, Alfred O. Hero III

Incorporating spatial information into hyperspectral unmixing procedures has been shown to have positive effects, due to the inherent spatial-spectral duality in hyperspectral scenes.

Hyperspectral Unmixing

Online dictionary learning for kernel LMS. Analysis and forward-backward splitting algorithm

no code implementations22 Jun 2013 Wei Gao, Jie Chen, Cédric Richard, Jianguo Huang

Unfortunately, an undesirable characteristic of these methods is that the order of the filters grows linearly with the number of input data.

Dictionary Learning

Distributed dictionary learning over a sensor network

no code implementations12 Apr 2013 Pierre Chainais, Cédric Richard

We consider the problem of distributed dictionary learning, where a set of nodes is required to collectively learn a common dictionary from noisy measurements.

Dictionary Learning regression

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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