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Variational bayesian compressive multipolarization indoor radar imaging / Van Ha Tang in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 9 (September 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 59 n° 9 (September 2021) . - pp 7459 - 7474 Titre : Variational bayesian compressive multipolarization indoor radar imaging Type de document : Article/Communication Auteurs : Van Ha Tang, Auteur ; Abdesselam Bouzerdoum, Auteur ; Son Lam Phung, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 7459 - 7474 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] acquisition comprimée [Termes IGN] détection à travers-le-mur [Termes IGN] estimation bayesienne [Termes IGN] fouillis d'échos [Termes IGN] image radar [Termes IGN] inférence statistique [Termes IGN] modèle stochastique [Termes IGN] polarisation [Termes IGN] positionnement en intérieur [Termes IGN] reconstruction d'image Résumé : (auteur) This article introduces a probabilistic Bayesian model for addressing the problem of compressive multipolarization through-wall radar imaging (TWRI). The proposed approach formulates the task of wall-clutter mitigation and multipolarization image reconstruction as a Bayesian inference problem for a joint distribution between observed radar measurements and latent wall-clutter matrix and indoor target images. The joint probability distribution incorporates three prior beliefs: low-dimensional structure of the wall reflections, group sparsity structure of the target images, and joint sparsity among the polarization images. These signal attributes are modeled through hierarchical priors, whose parameters and hyperparameters are treated with a full Bayesian formulation. Furthermore, this article presents a variational Bayesian inference algorithm that estimates wall-clutter and multipolarization images as posterior distributions and optimizes the model parameters and hyperparameters simultaneously. Experimental results on simulated and real radar data show that the proposed model is very effective at removing wall clutter and enhancing target localization even when the radar measurements are significantly reduced. Numéro de notice : A2021-647 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2021.3051955 Date de publication en ligne : 26/01/2021 En ligne : https://doi.org/10.1109/TGRS.2021.3051955 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98354 [article]

Compressive Sensing appliqué au traitement de données InSAR pour le suivi de la déformation des zones urbaines / Matthieu Rebmeister in XYZ, n° 166 (mars 2021)

Public [article]  inXYZ > n° 166 (mars 2021) . - pp 50 - 56 Titre : Compressive Sensing appliqué au traitement de données InSAR pour le suivi de la déformation des zones urbaines Type de document : Article/Communication Auteurs : Matthieu Rebmeister, Auteur Année de publication : 2021 Article en page(s) : pp 50 - 56 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] acquisition comprimée [Termes IGN] estimation des paramètres [Termes IGN] image radar moirée [Termes IGN] image TerraSAR-X [Termes IGN] reconstruction 3D du bâti [Termes IGN] site urbain [Termes IGN] télédétection en hyperfréquence Résumé : (Auteur) Les méthodes de traitement du signal, dites de“Compressive Sensing”, ont été développées vers la fin des années 1990 afin de résoudre des systèmes linéaires sous-déterminés. Il est envisageable d’appliquer ces nouveaux algorithmes aux images SAR à cause de leur géométrie d’acquisition particulière. À partir de plusieurs scènes SAR, il est possible de reconstruire la hauteur de chaque pixel possédant au minimum un réflecteur dominant et d’estimer la vitesse de déformation linéaire ainsi que la dilatation thermique des points concernés. Le problème est mal conditionné, ce qui signifie que sa résolution via les algorithmes de Compressive Sensing ne suffit pas à obtenir une solution robuste et plusieurs traitements doivent être effectués pour améliorer le résultat fourni. Un algorithme de traitement complet a été développé et est présenté dans cet article. Afin de tester son efficacité, celui-ci est appliqué sur des données issues du satellite TerraSAR-X. Les résultats montrent que l’estimation est cohérente avec le contexte topographique et urbain. L’algorithme développé permet ainsi de reconstruire en 3D et de suivre le déplacement des zones étudiées. Numéro de notice : A2021-248 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtSansCL DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97327 [article]

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Model based signal processing techniques for nonconventional optical imaging systems / Daniele Picone (2021)  

Public Titre : Model based signal processing techniques for nonconventional optical imaging systems Type de document : Thèse/HDR Auteurs : Daniele Picone, Auteur ; Mauro Dalla Mura, Directeur de thèse Editeur : Grenoble [France] : Université Grenoble Alpes Année de publication : 2021 Importance : 364 p. Format : 21 x 30 cm Note générale : bibliographie Thèse pour obtenir le grade de Docteur de l'Université Grenoble Alpes, spécialité : Signal Image Parole Télécoms Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] acquisition comprimée [Termes IGN] fusion d'images [Termes IGN] image hyperspectrale [Termes IGN] inférence statistique [Termes IGN] interférométrie [Termes IGN] méthode du maximum de vraisemblance (estimation) [Termes IGN] mosaïque d'images [Termes IGN] pouvoir de résolution géométrique [Termes IGN] pouvoir de résolution spectrale [Termes IGN] problème inverse [Termes IGN] reconstruction d'image [Termes IGN] régression non linéaire [Termes IGN] spectromètre imageur [Termes IGN] traitement du signal Index. décimale : THESE Thèses et HDR Résumé : (auteur) There is an increasing demand for images with higher spectral and spatial resolution for applications in several domains such as health, environment, quality checking and natural disasters monitoring. Hyperspectral imagery provides the necessary spectral diversity to recover the composition of materials on site for applications such as the detection of fires, anomalies, chemical agents, targets and changes in the scene.The requirement for cheaper and more compact devices (e.g. to be embarked on low cost satellites and airborne platform) which are capable of capturing this information has led to the development of nonconventional innovative design concepts to overcome the technological limitations of traditional cameras. Data acquired by such novel imaging devices following the computational imaging paradigm are typically not readily exploitable for the final application. A computational phase is hence needed for extracting useful information from the raw acquisitions.This thesis addresses this issue by setting up an inversion problem. The general approach is to characterize the data fidelity term with a physical model, describing the underlying optical transformations performed by the device. The challenge is then shifted on the regularization step to properly characterizes the features of the quantities of interest and improve the accuracy of the estimation, which can be tackled with variational techniques.The analysis is applied to two novel concepts for nonconventional optical devices. The first one is a novel compressed acquisition imaging system based on color filter arrays, which embeds information from sensors with different spatial and spectral characteristics into a single mosaiced product. As opposed to existing compressed sensing based devices, the goal is not to recover the original uncompressed multiresolution sources, but instead to directly recover a synthetic fused image with both high spatial and spectral resolution.The proposed solution relies on the total variation regularization and is the subject of a detailed analysis, comparing its compressive power with straightforward software alternatives, evaluating its performances as the amount of channels changes, and validating its efficiency in comparison to state of the art methods when applied to classical fusion or mosaicing algorithms separately.The second class of devices is based on the ImSPOC patent, a design concept for a low finesse snapshot imaging spectrometer based on the interferometry of Fabry-Pérot. Its ideal behaviour follows the principle of the Fourier Transform Spectroscopy, as its acquisition can be interpreted as a sampled version of an interferogram, arranged across different sub-images distributed on the same focal plane.After defining a physical model based on optical geometry, its validity is evaluated over real acquisitions by setting up a Bayesian inference problem to determine its parameters, with approaches based on maximum likelihood estimators, regular-grid searches and nonlinear regression.A variety of preliminary tests are then carried out on the inversion method, with approaches based on singular value decomposition and sparse-inducing regularizers, accompanied by a analysis of their robustness to model mismatches. Note de contenu : 1- Introduction 2- Inverse problems theory 3- Signal processing of multimodal data 4- Joint fusion and demosaicing of compressed multiresolution acquisitions 5- Optics foundations for the ImSPOC acquisition system 6- Data processing pipeline of ImSPOC acquisitions 7- Conclusions Numéro de notice : 28691 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Signal Image Parole Télécoms : Grenoble : 2021 Organisme de stage : GIPSA-lab DOI : sans En ligne : https://hal.science/tel-03596486v1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100170

Introducing spatial regularization in SAR tomography reconstruction / Clément Rambour in IEEE Transactions on geoscience and remote sensing, vol 57 n° 11 (November 2019)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 57 n° 11 (November 2019) . - pp 8600 - 8617 Titre : Introducing spatial regularization in SAR tomography reconstruction Type de document : Article/Communication Auteurs : Clément Rambour, Auteur ; Loïc Denis, Auteur ; Florence Tupin, Auteur ; Hélène Oriot, Auteur Année de publication : 2019 Article en page(s) : pp 8600 - 8617 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] acquisition comprimée [Termes IGN] analyse spectrale [Termes IGN] écho radar [Termes IGN] fractionnement [Termes IGN] image à très haute résolution [Termes IGN] image radar moirée [Termes IGN] image TerraSAR-X [Termes IGN] mécanique de Lagrange [Termes IGN] reconstruction 3D du bâti [Termes IGN] scène urbaine [Termes IGN] TerraSAR-X [Termes IGN] tomographie radar Résumé : (auteur) The resolution achieved by current synthetic aperture radar (SAR) sensors provides a detailed visualization of urban areas. Spaceborne sensors such as TerraSAR-X can be used to analyze large areas at a very high resolution. In addition, repeated passes of the satellite give access to temporal and interferometric information on the scene. Because of the complex 3-D structure of urban surfaces, scatterers located at different heights (ground, building facade, and roof) produce radar echoes that often get mixed within the same radar cells. These echoes must be numerically unmixed in order to get a fine understanding of the radar images. This unmixing is at the core of SAR tomography. SAR tomography reconstruction is generally performed in two steps: 1) reconstruction of the so-called tomogram by vertical focusing, at each radar resolution cell, to extract the complex amplitudes (a 1-D processing) and 2) transformation from radar geometry to ground geometry and extraction of significant scatterers. We propose to perform the tomographic inversion directly in ground geometry in order to enforce spatial regularity in 3-D space. This inversion requires solving a large-scale nonconvex optimization problem. We describe an iterative method based on variable splitting and the augmented Lagrangian technique. Spatial regularizations can easily be included in this generic scheme. We illustrate, on simulated data and a TerraSAR-X tomographic data set, the potential of this approach to produce 3-D reconstructions of urban surfaces. Numéro de notice : A2019-596 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2921756 Date de publication en ligne : 04/07/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2921756 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94588 [article]

Sparse signal modeling: Application to image compression, Image error concealment and compressed sensing / Ali Akbari (2018)  

Public Titre : Sparse signal modeling: Application to image compression, Image error concealment and compressed sensing Type de document : Thèse/HDR Auteurs : Ali Akbari, Auteur Editeur : Paris : Sorbonne Université Année de publication : 2018 Importance : 158 p. Format : 21 x 30 cm Note générale : bibliographie Thèse de Doctorat de Traitement du signal et de l’image, Sorbonne Université Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal [Termes IGN] acquisition comprimée [Termes IGN] compensation [Termes IGN] compression d'image [Termes IGN] modélisation [Termes IGN] problème inverse [Termes IGN] reconstruction d'image [Termes IGN] représentation parcimonieuse [Termes IGN] théorie du signal Index. décimale : THESE Thèses et HDR Résumé : (auteur) Signal models are a cornerstone of contemporary signal and image processing methodology. In this report, two particular signal modeling methods, called analysis and synthesis sparse representation, are studied which have been proven to be effective for many signals, such as natural images, and successfully used in a wide range of applications. Both models represent signals in terms of linear combinations of an underlying set, called dictionary, of elementary signals known as atoms. The driving force behind both models is sparsity of the representation coefficients, i.e. the rapid decay of the representation coefficients over the dictionary. On the other hands, the dictionary choice determines the success of the entire model. According to these two signal models, there have been two main disciplines of dictionary designing; harmonic analysis approach and machine learning methodology. The former leads to designing the dictionaries with easy and fast implementation, while the latter provides a simple and expressive structure for designing adaptable and efficient dictionaries. The main goal of this thesis is to provide new applications to these signal modeling methods by addressing several problems from various perspectives. It begins with the direct application of the sparse representation, i.e. image compression. The line of research followed in this area is the synthesis-based sparse representation approach in the sense that the dictionary is not fixed and predefined, but learned from training data and adapted to data, yielding a more compact representation. A new Image codec based on adaptive sparse representation over a trained dictionary is proposed, wherein different sparsity levels are assigned to the image patches belonging to the salient regions, being more conspicuous to the human visual system. Experimental results show that the proposed method outperforms the existing image coding standards, such as JPEG and JPEG2000, which use an analytic dictionary, as well as the state-of-the-art codecs based on the trained dictionaries. In the next part of thesis, it focuses on another important application of the sparse signal modeling, i.e. solving inverse problems, especially for error concealment (EC), wherein a corrupted image is reconstructed from the incomplete data, and Compressed Sensing recover, where an image is reconstructed from a limited number of random measurements. Signal modeling is usually used as a prior knowledge about the signal to solve these NP-hard problems. In this thesis, inspired by the analysis and synthesis sparse models, these challenges are transferred into two distinct sparse recovery frameworks and several recovery methods are proposed. Compared with the state-of-the-art EC and CS algorithms, experimental results show that the proposed methods show better reconstruction performance in terms of objective and subjective evaluations. This thesis is finalized by giving some conclusions and introducing some lines for future works. Note de contenu : 1- Introduction 2- Sparsity-based signal models 3- Image compressed sensing recovery 4- Receiver-based error concealment based on synthesis sparse recovery 5- Transmitter-based error concealment based on sparse recovery 6- Sparse representation-based image compression 7- Conclusion and future directions Numéro de notice : 25937 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Spécialité : Traitement du signal et de l’image : Paris : 2018 nature-HAL : Thèse DOI : sans En ligne : http://www.theses.fr/2018SORUS461 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96286

Multibaseline polarimetric synthetic aperture radar tomography of forested areas using wavelet-based distribution compressive sensing / Lei Liang in Journal of applied remote sensing, vol 9 (2015)  

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High-resolution fully polarimetric ISAR imaging based on compressive sensing / Wei Qiu in IEEE Transactions on geoscience and remote sensing, vol 52 n° 10 tome 1 (October 2014)  

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Joint wall mitigation and compressive sensing for indoor image reconstruction / E. Lagunas in IEEE Transactions on geoscience and remote sensing, vol 51 n° 2 (February 2013)  

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