Les propriétés optiques de nanotubes de carbone sont décrites idéalement parla physique d’un objet unidimensionnel, donnant lieu notamment à l’apparition des excitons pour décrire les transitions optiques de ces objets. Les expériences d’optique(émission, absorption) réalisées sur ces objets à température ambiante et sur des ensemble d’objets ont permis de confirmer les prédictions théoriques basées sur la physique des objets 1D. Mais à température cryogénique et à l’échelle de l’objet unique,les propriétés optiques observées expérimentalement sont systématiquement très éloignées de celles d’un objet 1D. On peut notamment citer l’apparition de propriétés comme l’émission de photons uniques, qui a largement contribué à l’intensification de la recherche sur ces objets pour des applications en photonique quantique. Ces propriétés sont attribuées à la localisation des excitons le long de l’axe des nanotubes dans des puits de potentiel créés aléatoirement par l’interaction des nanotubes avec leur environnement. Les propriétés optiques sont alors proches de celles des objets0D, et sont fortement modulées par l’environnement. Les mécanismes et l’origine de la localisation et la connaissance physique de ces puits sont encore très limités. Ce travail montre d’une part le développement d’une technique d’absorption sur objet individuel et la caractérisation de sa sensibilité, et d’autre part l’étude statistique de l’émission de nanotubes à température cryogénique. Les résultats obtenus par une technique de super-résolution couplée à une imagerie hyper-spectrale montrent les grandeurs caractéristiques des puits de potentiels au sein de nanotubes individuels.Un dispositif expérimental de photoluminescence résolue en excitation implémenté au cours de ce travail a également montré une modification de l’état excitonique fondamental par l’environnement, avec l’apparition d’une discrétisation spatiale et spectrale de l’état fondamental délocalisé en une multitude d’états localisés. / The optical properties of carbon nanotubes are ideally described by the physicsof a one-dimensional object, giving rise in particular to the emergence of excitons todescribe the optical transitions of these objects. The optical experiments (emission,absorption) carried out on these objects at ambient temperature and on ensemblesconfirm the theoretical predictions based on the physics of 1D objects. But atcryogenic temperature and at the single emitter scale, the optical properties observedexperimentally are systematically different from those of a 1D object. One can citethe emergence of properties such as photon antibunching, which largely contributed tothe intensification of research on these objects for applications in quantum photonics.These properties are attributed to the localization of excitons along the nanotube axisin local potential wells (traps) created randomly by the interaction of nanotubes withtheir environment. The optical properties are then close to those of 0D objects, andare strongly modulated by the environment. The mechanisms and the origin of thelocalization and the physical knowledge of these traps are still very limited. This workshows on the one hand the development of an absorption setup on individual objectand the characterization of its sensitivity, and on the other hand the statistical studyof the emission of nanotubes at cryogenic temperature in a micro-photoluminescencesetup. The results obtained in the later setup by a super-resolution technique coupledwith hyper-spectral imaging show the characteristic quantities of potential wellswithin individual nanotubes. An experimental excitation-resolved photoluminescencesetup implemented during this work also showed a modification of the fundamentalexcitonic state by the environment, with the emergence of a spatial and spectraldiscretization of the delocalized ground state in a multitude of localized states.

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The large cache of scrolls from Herculaneum were opened to scholars in spite of and because of the destruction of the volcano and damaging unfurling techniques. The fragments inherited have been studied closely by scholars. Digitization and technological work on the Herculaneum papyri, including the important infrared imaging completed by BYU in the early 2000s, and the 3-D imaging and inchoate virtual unwrapping technology by EduceLab, have amplified and aided scholarship on the scrolls and will continue to do so. P.Herc. 817 is a unique Latin text within the collection that has been heavily studied by scholars due to its fascinating subject matter on the Battle of Actium and Cleopatra and its readability. This fragment serves as a case study to demonstrate the value of each set of digital images in transcription and interpretation research, which suggests at the value of the varying digital images for other Herculaneum fragments. I closely compare digital surrogates of P.Herc. 817 including the early 2000s infrared images, 1960's era negatives, and recent 3-D images with the original artifacts as seen at the Biblioteca Nazionale in July 2023. This autopsy of versions of P.Herc. 817 substantiates the need for scholars to use all available digital images in concert with the original papyri when doing scholarly work. It also reinforces the need for digital stewardship and preservation of each distinct image set. Finally, a hypothetical case study is offered to show the loss to scholarship if the digital images and originals were lost and solely secondary sources remained. Each image set offers value and captures a moment in time of the papyri. As technology continues to progress and excitingly unlocks unseen papyri, care needs to be taken to safeguard and digitally preserve the new along with the older data sets.

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En Viticulture de Précision, l’imagerie multi-spectrale est principalement utilisée pour des dispositifs de télédétection. Ce manuscrit s’intéresse à son utilisation en proxidétection, pour la caractérisation du feuillage. Il présente un dispositif expérimental terrestre mobile composé d’un GPS, d’une caméra multi-spectrale acquérant des images visible et proche infrarouge, et d’un Greenseeker RT-100 mesurant l’indice Normalized Difference Vegetation Index (NDVI). Ce système observe le feuillage de la vigne dans le plan de palissage, en lumière naturelle. La parcelle étudiée comporte trois cépages (Pinot Noir, Chardonnay et Meunier) plantés en carré latin. En 2013, six jeux de données ont été acquis à différents stades phénologiques.Pour accéder aux propriétés spectrales de la végétation, il est nécessaire de calibrer les images en réflectance. Cela requiert l’utilisation d’une mire de MacBeth comme référence radiométrique. Lorsque la mire est cachée par les feuilles, les paramètres de calibration sont estimés par une interpolation linéaire en fonction des images les plus proches sur lesquelles la mire est visible. La cohérence de la méthode d’estimation employée est vérifiée par une validation croisée (LOOCV).La comparaison du NDVI fournie par le Greenseeker avec celui déterminé via les images corrigées permet de valider les données générées par le dispositif. La polyvalence du système est évaluée via les images où plusieurs indices de végétation sont déterminés. Ils permettant des suivis de croissance de la végétation originaux offrant des potentialités de phénotypage ou une caractérisation de l’état sanitaire de la végétation illustrant la polyvalence et le gain en précision de cette technique. / Mutispectral imaging systems are widely used in remote sensing for Precision Viticulture. In this work, this technique was applied in the proximal sensing context to characterize vine foliage. A mobile terrestrial experimental system is presented, composed of a GPS receiver, a multi-spectral camera acquiring visible and near infrared images, and a Greenseeker RT-100 which measures the Normalized Difference Vegetative Index (NDVI). This optical system observes vine foliage in the trellis plan, in natural sunlight. The experimental field is planted with Chardonnay, Pinot Noir and Meunier cultivars in a latin squared pattern. In 2013, six datasets were acquired at various phenological stages.Spectral properties of the vegetation are accessible on images when they are calibrated in reflectance. This step requires the use of a MacBeth colorchart as a radiometric reference. When the chart is hidden by leaves, the calibration parameters are estimated by simple linear interpolation using the results from resembling images, which have a visible chart. The performance of this method is verified with a cross-validation technique (LOOCV).To validate the data provided by the experimental system, the NDVI given by the Greenseeker was compared to those computed from the calibrated images. The assessment of the versatility of the system is done with the images where several indices were determined. It allows an innovative follow-up of the vegetative growth, and offering phenotyping applications. Moreover, the characterization of the sanitary state of the foliage prove that this technique is versatile and accurate.

In the past decade, camera manufacturers have offered smaller form factors, smaller pixel sizes (leading to higher resolution images), and faster processing chips to increase the performance of consumer cameras. However, these conventional approaches have failed to capitalize on the spatio-temporal redundancy inherent in images, nor have they adequately provided a solution for finding $3$D point correspondences for cameras sampling different bands of the visible spectrum. In this thesis, we pose the following question---given the repetitious nature of image patches, and appropriate camera architectures, can statistical models be used to increase temporal, structural, or spectral resolution? While many techniques have been suggested to tackle individual aspects of this question, the proposed solutions either require prohibitively expensive hardware modifications and/or require overly simplistic assumptions about the geometry of the scene. We propose a two-stage solution to facilitate image reconstruction; 1) design a linear camera system that optically encodes scene information and 2) recover full scene information using prior models learned from statistics of natural images. By leveraging the tendency of small regions to repeat throughout an image or video, we are able to learn prior models from patches pulled from exemplar images. The quality of this approach will be demonstrated for two application domains, using low-speed video cameras for high-speed video acquisition and multi-spectral fusion using an array of cameras. We also investigate a conventional approach for finding 3D correspondence that enables a generalized assorted array of cameras to operate in multiple modalities, including multi-spectral, high dynamic range, and polarization imaging of dynamic scenes.

Color theory was first formalized in the seventeenth century by Isaac Newton just a couple of decades after the first microscope was built. But it was not until the twentieth century that technological advances led to the integration of color theory, optical spectroscopy and light microscopy through spectral image processing. However, while the focus of image processing often concerns modeling of how images are perceived by humans, the goal of image processing in natural sciences and medicine is the objective analysis. This thesis is focused on color theory that promotes quantitative analysis rather than modeling how images are perceived by humans. Color and fluorescent dyes are routinely added to biological specimens visualizing features of interest. By applying spectral image processing to histopathology, subjectivity in diagnosis can be minimized, leading to a more objective basis for a course of treatment planning. Also, mathematical models for spectral image processing can be used in biotechnology research increasing accuracy and throughput, and decreasing bias. This thesis presents a model for spectral image formation that applies to both fluorescence and transmission light microscopy. The inverse model provides estimates of the relative concentration of each individual component in the observed mixture of dyes. Parameter estimation for the model is based on decoupling light intensity and spectral information. This novel spectral decomposition method consists of three steps: (1) photon and semiconductor noise modeling providing smoothing parameters, (2) image data transformation to a chromaticity plane removing  intensity variation while maintaining chromaticity differences, and (3) a piecewise linear decomposition combining advantages of spectral angle mapping and linear decomposition yielding relative dye concentrations. The methods described herein were used for evaluation of molecular biology techniques as well as for quantification and interpretation of image-based measurements. Examples of successful applications comprise quantification of colocalization, autofluorescence removal, classification of multicolor rolling circle products, and color decomposition of histological images.

Fast detection and identification of unknown substances is an area of interest for many parties. Raman spectroscopy is a laser-based method allowing for long range no contact investigation of substances. A Coded Aperture Snapshot Spectral Imaging (CASSI) system allows for fast and efficient measurements of hyperspectral images of a scene, containing a mixture of the spatial and spectral data. To analyze the scene and the unknown substances within it, it is required that the spectra in each spatial position are known. Utilizing the theory of compressed sensing allows for reconstruction of hyperspectral images of a scene given their CASSI measurements by assuming a sparsity prior. These reconstructions can then be utilized by a human operator to deduce and classify the unknown substances and their spatial locations in the scene. Such classifications are then applicable as decision support in various areas, for example in the judicial system. Reconstruction of hyperspectral images given CASSI-measurements is an ill-posed inverse problem typically solved by utilizing regularization techniques such as total variation (TV). These TV-based reconstruction methods are time consuming relative to the time needed to acquire the CASSI measurements, which is in the order of seconds. This leads to a reduced number of areas where the technology is applicable. In this thesis, a Generative Adversarial Network (GAN) based reconstruction method is proposed. A GAN is trained using simulated training data consisting of hyperspectral images and their respective CASSI measurements. The GAN provides a learned prior, and is used in an iterative optimization algorithm seeking to find an optimal set of latent variables such that the reconstruction error is minimized. The results of the developed GAN based reconstruction method are compared with a traditional TV method and a different machine learning based reconstruction method.  The results show that the reconstruction method developed in this thesis performs better than the compared methods in terms of reconstruction quality in short time spans.

Die hyperspektrale Bildgebung stellt eine Schlüsseltechnologie in der nicht-invasiven Mineralanalyse dar, sei es im Labormaßstab oder als fernerkundliche Methode. Rasante Entwicklungen im Sensordesign und in der Computertechnik hinsichtlich Miniaturisierung, Bildauflösung und Datenqualität ermöglichen neue Einsatzgebiete in der Erkundung mineralischer Rohstoffe, wie die drohnen-gestützte Datenaufnahme oder digitale Aufschluss- und Bohrkernkartierung. Allgemeingültige Datenverarbeitungsroutinen fehlen jedoch meist und erschweren die Etablierung dieser vielversprechenden Ansätze. Besondere Herausforderungen bestehen hinsichtlich notwendiger radiometrischer und geometrischer Datenkorrekturen, der räumlichen Georeferenzierung sowie der Integration mit anderen Datenquellen. Die vorliegende Arbeit beschreibt innovative Arbeitsabläufe zur Lösung dieser Problemstellungen und demonstriert die Wichtigkeit der einzelnen Schritte. Sie zeigt das Potenzial entsprechend prozessierter spektraler Bilddaten für komplexe Aufgaben in Mineralexploration und Geowissenschaften. / Hyperspectral imaging (HSI) is one of the key technologies in current non-invasive material analysis. Recent developments in sensor design and computer technology allow the acquisition and processing of high spectral and spatial resolution datasets. In contrast to active spectroscopic approaches such as X-ray fluorescence or laser-induced breakdown spectroscopy, passive hyperspectral reflectance measurements in the visible and infrared parts of the electromagnetic spectrum are considered rapid, non-destructive, and safe. Compared to true color or multi-spectral imagery, a much larger range and even small compositional changes of substances can be differentiated and analyzed. Applications of hyperspectral reflectance imaging can be found in a wide range of scientific and industrial fields, especially when physically inaccessible or sensitive samples and processes need to be analyzed. In geosciences, this method offers a possibility to obtain spatially continuous compositional information of samples, outcrops, or regions that might be otherwise inaccessible or too large, dangerous, or environmentally valuable for a traditional exploration at reasonable expenditure. Depending on the spectral range and resolution of the deployed sensor, HSI can provide information about the distribution of rock-forming and alteration minerals, specific chemical compounds and ions. Traditional operational applications comprise space-, airborne, and lab-scale measurements with a usually (near-)nadir viewing angle. The diversity of available sensors, in particular the ongoing miniaturization, enables their usage from a wide range of distances and viewing angles on a large variety of platforms. Many recent approaches focus on the application of hyperspectral sensors in an intermediate to close sensor-target distance (one to several hundred meters) between airborne and lab-scale, usually implying exceptional acquisition parameters. These comprise unusual viewing angles as for the imaging of vertical targets, specific geometric and radiometric distortions associated with the deployment of small moving platforms such as unmanned aerial systems (UAS), or extreme size and complexity of data created by large imaging campaigns. Accurate geometric and radiometric data corrections using established methods is often not possible. Another important challenge results from the overall variety of spatial scales, sensors, and viewing angles, which often impedes a combined interpretation of datasets, such as in a 2D geographic information system (GIS). Recent studies mostly referred to work with at least partly uncorrected data that is not able to set the results in a meaningful spatial context. These major unsolved challenges of hyperspectral imaging in mineral exploration initiated the motivation for this work. The core aim is the development of tools that bridge data acquisition and interpretation, by providing full image processing workflows from the acquisition of raw data in the field or lab, to fully corrected, validated and spatially registered at-target reflectance datasets, which are valuable for subsequent spectral analysis, image classification, or fusion in different operational environments at multiple scales. I focus on promising emerging HSI approaches, i.e.: (1) the use of lightweight UAS platforms, (2) mapping of inaccessible vertical outcrops, sometimes at up to several kilometers distance, (3) multi-sensor integration for versatile sample analysis in the near-field or lab-scale, and (4) the combination of reflectance HSI with other spectroscopic methods such as photoluminescence (PL) spectroscopy for the characterization of valuable elements in low-grade ores. In each topic, the state of the art is analyzed, tailored workflows are developed to meet key challenges and the potential of the resulting dataset is showcased on prominent mineral exploration related examples. Combined in a Python toolbox, the developed workflows aim to be versatile in regard to utilized sensors and desired applications.

<p>Raman spectroscopy is a molecular spectroscopy technique that uses monochromatic light to provide a fingerprint to identify structural components and chemical composition. Depending on the changes in the unit-cell parameters and volume under the application of stress and temperature, the Raman spectrum undergoes changes in the wavenumber of Raman-active modes that allow identification of sample characteristics. Due to the various advantage of mechanical Raman spectroscopy (MRS), the use of this technique in the characterization and modeling of chemical changes under stress and temperature have gained popularity. </p> <p> Quantitative information regarding the local behavior of interfaces in an inhomogeneous material during shock loading is limited due to challenges associated with time and spatial resolution. Recently, we have extended the use of MRS to high-strain rate experiments to capture the local thermomechanical response of mock energetic material and obtain material properties during shock wave propagation. This was achieved by developing a novel method for <i>in‑situ</i> measurement of the thermo‑mechanical response from mock energetic materials in a time‑resolved manner with 5 ns resolution providing an estimation on local pressure, temperature, strain rate, and local shock viscosity. The results show the solid to liquid phase transition of sucrose under shock compression. The viscous behavior of the binder was also characterized through measurement of shock viscosity at strain rates higher than 10⁶/s using microsphere impact experiments.</p> <p> This technique was further extended to perform Raman spectral imaging over a microscale domain of the sample with a nano-second resolution. This was achieved by developing a laser-array Raman spectral imaging technique where simultaneous deconvolution of Raman spectra over the sample domain was achieved and Raman spectral image was reconstructed on post-processing. We developed a Raman spectral imaging system using a laser array and analysis was performed over the interface of sucrose crystals bonded using an epoxy binder. This study provides the Raman spectra over the microstructure domain which enabled the detection of localized melting under shock compression. The distribution of shock pressure and temperature over the microstructure was obtained using mechanical Raman analysis. The study shows the effects of an actual interface on the propagation of shock waves where a higher dissipation of shock energy was observed compared to an ideal interface. This increase in shock dissipation is accompanied by a decrease in both the maximum temperature, as well as the maximum pressure within the microstructure during shock wave propagation.</p>