WANG , Xiuheng

In the era of artificial intelligence, there has been a growing consensus that solutions to complex science and engineering problems require novel methodologies that can integrate interpretable physics-based modeling approaches with machine learning techniques, from stochastic optimization to deep neural networks. This thesis aims to develop...

Deconvolution is a widely used strategy to mitigate the blurring and noisy degradation of hyperspectral images (HSIs) generated by the acquisition devices. This issue is usually addressed by solving an ill-posed inverse problem. While investigating proper image priors can enhance the deconvolution performance, it is not trivial to handcraft a...

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. This technique aims to fuse a low-resolution (LR) HSI and a conventional high-resolution (HR) RGB image in order to obtain an HR HSI....

Online detection of abrupt changes in streaming time series is a challenging problem with many applications, in particular when little prior knowledge of the statistics of the data is available and computation resources are scarce. While many algorithms have been developed for Euclidean spaces, there is a wealth of data that belongs to...

Non-parametric detection of change points in streaming time series data that belong to Euclidean spaces has been extensively studied in the literature. Nevertheless, when the data belongs to a Riemannian manifold, existing approaches are no longer applicable as they fail to account for the structure and geometry of the manifold. In this paper,...

Distributed adaptation and learning recently gained considerable attention in solving optimization problems with streaming data collected by multiple agents over a graph. This work focuses on such problems where the solutions lie on a Riemannian manifold. This research topic is of particular interest for many applications, e.g., principal...

Hyperspectral image (HSI) super-resolution is commonly used to overcome the hardware limitations of existing hyperspectral imaging systems on spatial resolution. It fuses a low-resolution (LR) HSI and a high-resolution (HR) conventional image of the same scene to obtain an HR HSI. In this work, we propose a method that integrates a physical...

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...

Plusieurs méthodes de détection de changements dans des signaux sur graphe ont été proposées dans la littérature. Cependant, si elles exploitent à bon escient la topologie des graphes, elles se limitent au traitement de séries temporelles sur graphe dans des espaces euclidiens. Dans cet article, nous proposons une méthode de détection de...

Detecting change points in streaming time series data is a long standing problem in signal processing. A plethora of methods have been proposed to address it, depending on the hypotheses at hand. Non-parametric approaches are particularly interesting as they do not make any assumption on the distribution of data or on the nature of changes....

Hyperspectral and multispectral image fusion (HMIF) allows us to overcome inherent hardware limitations of hyperspectral imaging systems with respect to their lower spatial resolution. However, existing algorithms fail to consider realistic image acquisition conditions, or to leverage the powerful representation capacity of deep neural...

Hyperspectral image (HI) and multispectral image (MI) fusion allows us to overcome the hardware limitations of hyperspectral imaging systems inherent to their lower spatial resolution. Nevertheless, existing algorithms usually fail to consider realistic image acquisition conditions. This article presents a general imaging model that considers...

Signal processing methods over graphs and networks have recently been proposed to detect change points occurring in localized communities of nodes. Nevertheless, all these methods are mostly limited to time series data in Euclidean spaces. In this paper, we devise a distributed change point detection method for streaming manifold-valued signals...

Spectral unmixing is central when analyzing hyperspectral data. To accomplish this task, physics-based methods have become popular because, with their explicit mixing models, they can provide a clear interpretation. Nevertheless, because of their limited modeling capabilities, especially when analyzing real scenes with unknown complex physical...