No description available.

L’imagerie d’exoplanètes permet d’obtenir de nombreuses informations sur la lumière qu’elles émettent, l’interaction avec leur environnement et sur leur nature. Afin d’extraire l’information des images, il est indispensable d’appliquer des méthodes de traitement d’images adaptées aux instruments. En particulier, il faut séparer les signaux planétaires des tavelures présentes dans les images qui sont dues aux aberrations instrumentales quasi-statiques. Dans mon travail de thèse je me suis intéressée à deux méthodes innovantes de traitement d’images qui sont fondés sur la résolution de problèmes inverses.La première méthode, ANDROMEDA, est un algorithme dédié à la détection et à la caractérisation de point sources dans des images haut contraste via une approche maximum de vraisemblance. ANDROMEDA exploite la diversité temporelle apportée par la rotation de champ de l’image (où se trouvent les objets astrophysiques) alors que la pupille (où les aberrations prennent naissance) est gardée fixe. A partir de l’application sur données réelles de l’algorithme dans sa version originale, j’ai proposé et qualifié des améliorations afin de prendre en compte les résidus non modélisés par la méthode tels que les structures bas ordres variant lentement et le niveau résiduel de bruit correlé dans les données. Une fois l’algorithme ANDROMEDA opérationnel, j’ai analysé ses performances et sa sensibilité aux paramètres utilisateurs, montrant la robustesse de la méthode. Une comparaison détaillée avec les algorithmes les plus utilisés dans la communauté a prouvé que cet algorithme est compétitif avec des performances très intéressantes dans le contexte actuel. En particulier, il s’agit de la seule méthode qui permet une détection entièrement non-supervisée. De plus, l’application à de nombreuses données ciel venant d’instruments différents a prouvé la fiabilité de la méthode et l’efficacité à extraire rapidement et systématiquement (avec un seul paramètre utilisateur à ajuster) les informations contenues dans les images. Ces applications ont aussi permis d’ouvrir des perspectives pour adapter cet outil aux grands enjeux actuels de l’imagerie d’exoplanètes.La seconde méthode, MEDUSAE, consiste à estimer conjointement les aberrations et les objets d’intérêt scientifique, en s’appuyant sur un modèle de formation d’images coronographiques. MEDUSAE exploite la redondance d’informations apportée par des images multi-spectrales. Afin de raffiner la stratégie d’inversion de la méthode et d’identifier les paramètres les plus critiques, j’ai appliqué l’algorithme sur des données générées avec le modèle utilisé dans l’inversion. J’ai ensuite appliqué cette méthode à des données simulées plus réalistes afin d’étudier l’impact de la différence entre le modèle utilisé dans l’inversion et les données réelles. Enfin, j’ai appliqué la méthode à des données réelles et les résultats préliminaires que j’ai obtenus ont permis d’identifier les informations importantes dont la méthode a besoin et ainsi de proposer plusieurs pistes de travail qui permettraient de rendre cet algorithme opérationnel sur données réelles. / Direct imaging of exoplanets provides valuable information about the light they emit, their interactions with their host star environment and their nature. In order to image such objects, advanced data processing tools adapted to the instrument are needed. In particular, the presence of quasi-static speckles in the images, due to optical aberrations distorting the light from the observed star, prevents planetary signals from being distinguished. In this thesis, I present two innovative image processing methods, both based on an inverse problem approach, enabling the disentanglement of the quasi-static speckles from the planetary signals. My work consisted of improving these two algorithms in order to be able to process on-sky images.The first one, called ANDROMEDA, is an algorithm dedicated to point source detection and characterization via a maximum likelihood approach. ANDROMEDA makes use of the temporal diversity provided by the image field rotation during the observation, to recognize the deterministic signature of a rotating companion over the stellar halo. From application of the original version on real data, I have proposed and qualified improvements in order to deal with the non-stable large scale structures due to the adaptative optics residuals and with the remaining level of correlated noise in the data. Once ANDROMEDA became operational on real data, I analyzed its performance and its sensitivity to the user-parameters proving the robustness of the algorithm. I also conducted a detailed comparison to the other algorithms widely used by the exoplanet imaging community today showing that ANDROMEDA is a competitive method with practical advantages. In particular, it is the only method that allows a fully unsupervised detection. By the numerous tests performed on different data set, ANDROMEDA proved its reliability and efficiency to extract companions in a rapid and systematic way (with only one user parameter to be tuned). From these applications, I identified several perspectives whose implementation could significantly improve the performance of the pipeline.The second algorithm, called MEDUSAE, consists in jointly estimating the aberrations (responsible for the speckle field) and the circumstellar objects by relying on a coronagraphic image formation model. MEDUSAE exploits the spectral diversity provided by multispectral data. In order to In order to refine the inversion strategy and probe the most critical parameters, I applied MEDUSAE on a simulated data set generated with the model used in the inversion. To investigate further the impact of the discrepancy between the image model used and the real images, I applied the method on realistic simulated images. At last, I applied MEDUSAE on real data and from the preliminary results obtained, I identified the important input required by the method and proposed leads that could be followed to make this algorithm operational to process on-sky data.

The Fourier transform (spectrum) of a signal is a complex function and is characterized by the magnitude and phase spectra. Phase retrieval is the reconstruction of the phase spectrum from the measurements of the magnitude spectrum. Such problems are encountered in imaging modalities such as X-ray crystallography, frequency-domain optical coherence tomography (FDOCT), quantitative phase microscopy, digital holography, etc., where only the magnitudes of the wavefront are detected by the sensors. The phase retrieval problem is ill-posed in general, since an in nite number of signals can have the same magnitude spectrum. Typical phase retrieval techniques rely on certain prior knowledge about the signal, such as its support or sparsity, to reconstruct the signal. A classical result in phase retrieval is that minimum-phase signals have log-magnitude and phase spectra that satisfy the Hilbert integral equations, thus facilitating exact phase retrieval. In this thesis, we demonstrate that there exist larger classes of signals beyond minimum-phase signals, for which exact phase retrieval is possible. We generalize Hilbert integral equations to 2-D, and also introduce a variant that we call the composite Hilbert transform in the context of 2-D periodic signals. Our first extension pertains to a particular type of parametric modelling of 2-D signals. While 1-D minimum-phase signals have a parametric representation, in terms of poles and zeros, there exists no such 2-D counterpart. We introduce a new class of parametric 2-D signals that possess the exact phase retrieval property, that is, their magnitude spectrum completely characterizes the signal. Starting from the magnitude spectrum, a sequence of non-linear operations lead us to a sum-of-exponentials signal, from which the parameters are computed employing concepts from high-resolution spectral estimation such as the annihilating filter and algebraically coupled matrix-pencil methods. We demonstrate that, for this new class of signals, our method outperforms existing techniques even in the presence of noise. Our second extension is to continuous-domain signals that lie in a principal shift-invariant space spanned by a known basis. Such signals are characterized by the basis combining coefficients. These signals need not be minimum-phase, but certain conditions on the coefficients lead to exact phase retrieval of the continuous-domain signal. In particular, we introduce the concept of causal, delta dominant (CDD) sequences, and show that such signals are characterized by their magnitude spectra. This condition pertains to the time/spatial-domain description of the signal, in contrast to the minimum-phase condition, which is described in the spectral domain. We show that there exist CDD sequences that are not minimum-phase, and vice versa. However, finite-length CDD sequences are always minimum-phase. Our method reconstructs the signal from the magnitude spectrum up to ma-chine precision. We thus have a class of continuous-domain signals that are neither causal nor minimum phase, and yet allow for exact phase retrieval. The shift-invariant structure is applicable to modelling signals encountered in imaging modalities such as FDOCT. We next present an application of 2-D phase retrieval to continuous-domain CDD signals in the context of quantiative phase microscopy. We develop sufficient conditions on the interfering reference wave for exact phase retrieval from magnitude measurements. In particular, we show that when the reference wave is a plane wave with magnitude greater that the intensity of the object wave, and when the carrier frequency is larger than the band-width of the object wave, we can reconstruct the object wave exactly. We demonstrate high-resolution reconstruction of our method on USAF target images. Our final and perhaps the most unifying contribution is in developing Hilbert integral equations for 2-D first-quadrant signals and in introducing the notion of generalized minimum-phase signals for both 1-D and 2-D signals. For 2-D continuous-domain, first-quadrant signals, we establish partial Hilbert transform relations between the real and imaginary parts of the spectrum. In the context of 2-D discrete-domain signals, we show that the partial Hilbert transform does not suffice and introduce the notion of composite Hilbert transform and establish the integral equations. We then introduce four classes of signals (combinations of 1-D/2-D and continuous/discrete-domain) that we call generalized minimum-phase signals, which satisfy corresponding Hilbert integral equations between log-magnitude and phase spectra, hence facilitating exact phase retrieval. This class of generalized minimum-phase signals subsumes the well known class of minimum-phase signals. We further show that, akin to minimum-phase signals, these signals also have stable inverses, which are also generalized minimum-phase signals.

No description available.

The availability of spatial light modulators as standard turnkey components and their ongoing development makes them attractive for a huge variety of optical measurement systems in industry and research. Here, we outline two examples of how optical measurements can benefit from spatial light modulators. Ultrasound testing has become an indispensable tool for industrial inspection. Contact-free measurements can be achieved by laser-induced ultrasound. One disadvantage is that due to the highly divergent sound field of the generated shear waves for a point-wise thermoelastic excitation, only a poor spatial selectivity can be achieved. This problem can be solved by creating an ultrasound focus by means of a ring-like laser intensity distribution, but standard fixed-form optical components used for their generation are always optimised to a fixed set of parameters. Here, we demonstrate, how a predefined intensity pattern as e.g. a ring can be created from an arbitrary input laser beam using a phase-retrieval algorithm to shape an ultrasound focus in the sample. By displaying different patterns on the spatial light modulator, the focus can be traversed in all three directions through the object allowing a fast and highly spatially resolving scanning of the sample. Optical measurements take often place under difficult conditions. They are affected by variations of the refractive index, caused e.g. by phase boundaries between two media of different optical density. This will result in an increased measurement uncertainty or, in the worst case, will cause the measurement to fail. To overcome these limitations, we propose the application of adaptive optics. Optical flow velocity measurements based on image correlation in water that are performed through optical distortions are discussed. We demonstrate how the measurement error induced by refractive index variations can be reduced if a spatial light modulator is used in the measurement setup to compensate for the wavefront distortions.

La façon dont une information se répète au cours du temps a une influence sur la façon dont nous nous souviendrons de cette information. Les recherches en psychologie ont mis en évidence l’effet de pratique distribuée, selon lequel on retient mieux les informations qui se répètent avec des intervalles inter-répétitions longs que celles qui se répètent avec des intervalles courts. Nos travaux ont porté spécifiquement sur les situations où l’information se répète sur plusieurs jours, et nous avons comparé l’efficacité relative de différents types d’agencement temporel des répétitions. Un agencement uniforme consiste en des répétitions se produisant à intervalles réguliers, un agencement expansif en des répétitions se produisant selon des intervalles de plus en plus espacés, et un agencement contractant en des répétitions se produisant selon des intervalles de plus en plus rapprochés. Les Expériences 1 et 2 consistaient en une phase d’apprentissage d’une semaine et ont révélé la supériorité des agencements expansif et uniforme après un délai de rétention de deux jours. L’Expérience 3 consistait en une phase d’apprentissage de deux semaines, et les sujets étaient ensuite testés lors de trois délais de rétention différents (2, 6 ou 13 jours). La supériorité de l’agencement expansif sur les deux autres agencements est apparue progressivement, suggérant que les différents agencements induisaient des taux d’oubli différents. Nous avons également tenté de tester différentes théories explicatives des effets de l’agencement temporel des répétitions sur la mémorisation, en particulier les théories de la variabilité de l’encodage (Expérience 4) et de la récupération en phase d’étude (Expérience 2). Les résultats observés tendent à confirmer la théorie de la récupération en phase d’étude. Nous insistons sur l’importance de la prise en compte des apports des autres disciplines des sciences cognitives dans l’étude de l’effet de pratique distribuée. / How information is repeated over time determines future recollection of this information. Studies in psychology revealed a distributed practice effect, that is, one retains information better when its occurrences are separated by long lags rather than by short lags. Our studies focused specifically on cases in which items were repeated upon several days. We compared the efficiency of three different temporal schedules of repetitions: A uniform schedule that consisted in repetitions occurring with equal intervals, an expanding schedule that consisted in repetitions occurring with longer and longer intervals, and a contracting schedule that consisted in repetitions occurring with shorter and shorter intervals. In Experiments 1 and 2, the learning phase lasted one week and the retention interval lasted two days. It was shown that the expanding and uniform schedules were more efficient than the contracting schedule. In Experiment 3, the learning phase lasted two weeks and the retention interval lasted 2, 6, or 13 days. It was shown that the superiority of the expanding schedule over the other two schedules appeared gradually when the retention interval increased, suggesting that different schedules yielded different forgetting rates. We also tried to test major theories of the distributed practice effect, such as the encoding variability (Experiment 4) and the study-phase retrieval (Experiment 2) theories. Our results appeared to be consistent with the study-phase retrieval theory. We concluded our dissertation by emphasizing the importance of considering findings from other areas in cognitive science–especially neuroscience and computer science–in the study of the distributed practice effect.

No description available.

Zur vollständigen Beschreibung eines monochromatischen Wellenfeldes ist die Kenntnis über die Amplituden- und Phasenverteilung unabdingbar. Während sich die messtechnische Erfassung der Amplitudenverteilung durch lichtempfindliche Sensoren recht einfach realisieren lässt, gestaltet sich die Bestimmung der Phasenverteilung weitaus schwieriger. Die Phasenverteilung eines optischen Wellenfeldes kann nur über indirekte Verfahren gewonnen werden. Es ergibt sich ein sogenanntes phase retrieval Problem. Zur Lösung dieses Problems bieten sich verschiedene Verfahren aus dem Bereich der berührungslosen und zerstörungsfreien optische Messtechnik an. In dieser Arbeit wird ein deterministisches Verfahren zur Phasenrekonstruktion mit Hilfe Greenscher Funktionen vorgestellt. Die erste Greensche Identität dient als Grundlage zur Entwicklung einer Gleichung, welche in der Lage ist, bei der Rekonstruktion einer Phasenverteilung spezifische Randbedingungen zu berücksichtigen. Dies ermöglicht unter anderem eine genaue Charakterisierung von Phasenobjekten bzw. ihren optischen Eigenschaften, wie beispielsweise der Brechzahlverteilung. Das vorgestellte Verfahren zur Phasenrekonstruktion basiert einerseits auf schnellen Algorithmen, welche die Leistung von parallelen Prozessoren ausnutzen und andererseits auf geschickten experimentellen Aufbauten, mit welchen die notwendigen Eingangsdaten zur Lösung der Gleichung simultan gewonnen werden können. Es ergibt sich damit die Möglichkeit, die Amplituden- und Phasenverteilung eines Wellenfeldes in Echtzeit zu bestimmen und daraus folgend ein Mittel zur quantitativen Bewertungen und Analyse von dynamischen Prozessen sowohl in der Industrie als auch im Bereich der Life Sciences. / In order to describe a monochromatic wave field entirely, knowledge about the amplitude and phase distribution is elementary. While it is easy to measure the amplitude distribution of an optical wave field by the use of photosensitive detectors, the determination of the phase distribution is by far more difficult. Due to the fact, that the phase distribution can not be measured directly, a problem of phase retrieval is presented. This problem may be solved by applying a non-contacting and non-destructive optical metrology technique. In this thesis a deterministic method for phase retrieval based on Green''s functions will be introduced. Green''s first identity serves as a starting point to derive an equation for phase retrieval considering different boundary conditions. Among others, this allows an exact characterization of phase objects, or their optical properties, as for example the refractive index distribution. On the one hand, the presented phase retrieval technique is based on fast algorithms which take advantage of the performance of parallel processors. On the other hand, skilful experimental setups allow the simultaneous acquisition of the input data, which are necessary to solve the phase retrieval equation. It follows that the presented technique is able to determine the amplitude and phase distribution of a wave field in real-time. Hence this technique enables the quantitative evaluation and analysis of dynamic processes in industry as well as in the area of life sciences.

No description available.

The development of highly coherent X-ray sources offers new possibilities to image biological structures at different scales exploiting the refraction of X-rays. The coherence properties of the third-generation synchrotron radiation sources enables efficient implementations of phase contrast techniques. One of the first measurements of the intensity variations due to phase contrast has been reported in 1995 at the European Synchrotron Radiation Facility (ESRF). Phase imaging coupled to tomography acquisition allows threedimensional imaging with an increased sensitivity compared to absorption CT. This technique is particularly attractive to image samples with low absorption constituents. Phase contrast has many applications, ranging from material science, paleontology, bone research to medicine and biology. Several methods to achieve X-ray phase contrast have been proposed during the last years. In propagation based phase contrast, the measurements are made at different sample-to-detector distances. While the intensity data can be acquired and recorded, the phase information of the signal has to be "retrieved" from the modulus data only. Phase retrieval is thus an illposed nonlinear problem and regularization techniques including a priori knowledge are necessary to obtain stable solutions. Several phase recovery methods have been developed in recent years. These approaches generally formulate the phase retrieval problem as a linear one. Nonlinear treatments have not been much investigated. The main purpose of this work was to propose and evaluate new algorithms, in particularly taking into account the nonlinearity of the direct problem. In the first part of this work, we present a Landweber type nonlinear iterative scheme to solve the propagation based phase retrieval problem. This approach uses the analytic expression of the Fréchet derivative of the phase-intensity relationship and of its adjoint, which are presented in detail. We also study the effect of projection operators on the convergence properties of the method. In the second part of this thesis, we investigate the resolution of the linear inverse problem with an iterative thresholding algorithm in wavelet coordinates. In the following, the two former algorithms are combined and compared with another nonlinear approach based on sparsity regularization and a fixed point algorithm. The performance of theses algorithms are evaluated on simulated data for different noise levels. Finally the algorithms were adapted to process real data sets obtained in phase CT at the ESRF at Grenoble.