Cette thèse traite du couplage d'un tomographe optique par luminescence (LCT) et d'un tomographe par rayons X (XCT), en présence d'une contrainte sur la géométrie d'acquisition du XCT. La couverture angulaire du XCT est limitée à 90 degrés pour satisfaire des contraintes spatiales imposées par le LCT existant dans lequel le XCT doit être intégré. L'objectif est de dériver une information anatomique, à partir de l'image morphologique issue du XCT. Notre approche a consisté i) en l'implémentation d'un algorithme itératif régularisé pour la reconstruction tomographique à angle limité, ii) en la construction d'un atlas anatomique statistique de la souris et iii) en l'implémentation d'une chaîne automatique réalisant la segmentation des images XCT, l'attribution d'une signification anatomique aux éléments segmentés, le recalage de l'atlas statistique sur ces éléments et ainsi l'estimation des contours de certains tissus à faible contraste non identifiables en pratique dans une image XCT standard. / This thesis addresses the combination of an Optical Luminescence Tomograph (OLT) and X-ray Computerized Tomograph (XCT), dealing with geometrical constraints defined by the existing OLT system in which the XCT must be integrated. The result is an acquisition geometry of XCT with a 90 degrees angular range only. The aim is to derive an anatomical information from the morphological image obtained with the XCT. Our approach consisted i) in the implementation of a regularized iterative algorithm for the tomographic reconstruction with limited angle data, ii) in the construction of a statistical anatomical atlas of the mouse and iii) in the implementation of an automatic segmentation workflow performing the segmentation of XCT images, the labelling of the segmented elements, the registration of the statistical atlas on these elements and consequently the estimation of the outlines of low contrast tissues that can not be identified in practice in a standard XCT image.

Variational methods and Partial Differential Equations (PDEs) have been extensively employed for the mathematical formulation of a myriad of problems describing physical phenomena such as heat propagation, thermodynamic transformations and many more. In imaging, PDEs following variational principles are often considered. In their general form these models combine a regularisation and a data fitting term, balancing one against the other appropriately. Total variation (TV) regularisation is often used due to its edgepreserving and smoothing properties. In this thesis, we focus on the design of TV-based models for several different applications. We start considering PDE models encoding higher-order derivatives to overcome wellknown TV reconstruction drawbacks. Due to their high differential order and nonlinear nature, the computation of the numerical solution of these equations is often challenging. In this thesis, we propose directional splitting techniques and use Newton-type methods that despite these numerical hurdles render reliable and efficient computational schemes. Next, we discuss the problem of choosing the appropriate data fitting term in the case when multiple noise statistics in the data are present due, for instance, to different acquisition and transmission problems. We propose a novel variational model which encodes appropriately and consistently the different noise distributions in this case. Balancing the effect of the regularisation against the data fitting is also crucial. For this sake, we consider a learning approach which estimates the optimal ratio between the two by using training sets of examples via bilevel optimisation. Numerically, we use a combination of SemiSmooth (SSN) and quasi-Newton methods to solve the problem efficiently. Finally, we consider TV-based models in the framework of graphs for image segmentation problems. Here, spectral properties combined with matrix completion techniques are needed to overcome the computational limitations due to the large amount of image data. Further, a semi-supervised technique for the measurement of the segmented region by means of the Hough transform is proposed.

The focus of this thesis is variational image restoration techniques that involve novel non-smooth first-order gradient regularisers: Total Variation (TV) regularisation in image and data space for reconstruction of thin structures from PET data and regularisers given by an infimal-convolution of TV and $L^p$ seminorms for denoising images with piecewise affine structures. In the first part of this thesis, we present a novel variational model for PET reconstruction. During a PET scan, we encounter two different spaces: the sinogram space that consists of all the PET data collected from the detectors and the image space where the reconstruction of the unknown density is finally obtained. Unlike most of the state of the art reconstruction methods in which an appropriate regulariser is designed in the image space only, we introduce a new variational method incorporating regularisation in image and sinogram space. In particular, the corresponding minimisation problem is formed by a total variational regularisation on both the sinogram and the image and with a suitable weighted $L^2$ fidelity term, which serves as an approximation to the Poisson noise model for PET. We establish the well-posedness of this new model for functions of Bounded Variation (BV) and perform an error analysis through the notion of the Bregman distance. We examine analytically how TV regularisation on the sinogram affects the reconstructed image especially the boundaries of objects in the image. This analysis motivates the use of a combined regularisation principally for reconstructing images with thin structures. In the second part of this thesis we propose a first-order regulariser that is a combination of the total variation and $L^p$ seminorms with $1 < p \le \infty$. A well-posedness analysis is presented and a detailed study of the one dimensional model is performed by computing exact solutions for simple functions such as the step function and a piecewise affine function, for the regulariser with $p = 2$ and $p = 1$. We derive necessary and sufficient conditions for a pair in $BV \times L^p$ to be a solution for our proposed model and determine the structure of solutions dependent on the value of $p$. In the case $p = 2$, we show that the regulariser is equivalent to the Huber-type variant of total variation regularisation. Moreover, there is a certain class of one dimensional data functions for which the regularised solutions are equivalent to high-order regularisers such as the state of the art total generalised variation (TGV) model. The key assets of our regulariser are the elimination of the staircasing effect - a well-known disadvantage of total variation regularisation - the capability of obtaining piecewise affine structures for $p = 1$ and qualitatively comparable results to TGV. In addition, our first-order $TVL^p$ regulariser is capable of preserving spike-like structures that TGV is forced to smooth. The numerical solution of the proposed first-order model is in general computationally more efficient compared to high-order approaches.

Dans cette thèse, nous présentons une nouvelle approche pour la simulation numérique des phénomènes électroconvectifs et électro-therrno-convectifs. La principale difficulté réside dans la détermination du champ électrique et de la distribution de densité volurnique de charges électriques. Dans cette approche, des schémas de type TVD (Total Variation Dirninishing) et IDC (ImprovedDeferred Correction) sont utilisées dans la discrétisation des flux convectifs et diffusifs par la méthode des volumes finis. La première partie de cette thèse présente certains aspects numériques liés à l'implémentation de ces schémas. Une approche unifiée pour les schémas convectifs TVD de type borné à haute résolution est présentée et diverses fonctions limiteur sont comparées. Dans une deuxième partie, l'électro-convection entre deux plaques parallèles est simulée. La méthodologie a étéévaluée et validée par la détermination des Critères de stabilité linéaire et non linéaire. Les différents scenarii d'évolution du développement de cette instabilité électroconvective vers l'état chaotique ont été définis. L'effet du mécanisme de diffusion la densité volumique de charge sur la boucle d'hystérésis et sur la structure de l'écoulement est étudié. L'influence du rapport d'aspect de la cavité est analysé. Enfin dans une dernière partie, nous étudions l'électro-thermo-convection lorsque le fluide est soumis simultanément à une injection unipolaire et à un gradient thermique. L'augmentation des transferts de chaleur a été caractérisée. / In this thesis, a numerical approach is presented to simulate the electro- and electro-thermo convection in dielectric liquids. The total variation diminishing (TVD) scheme and improved deferred correction (IDC) scheme are used to compute the convective and diffusive respectively. The aim of TVD scheme is to avoid non-physical oscillations and to capture high gradient of charge density. Some fundarnental aspects related to TVD and LDC schemes are investigated firstly. A unified approach for TVD schemes is explained and various limiter functions are compared. The connection among three methods for diffusive flux computation has been revealed. The original IDC scheme is improved by the application of 2nd order gradient evaluation method.The electro-convection between two parallel plates is then simulated. The methodology was assessed by the determination of the linear and nonlinear stability criterion. By continuously increasing the driving parameter, the successive instabilities and route to chaotic state has been defined. The effects of the diffusion mechanism for the charge density and vertical walls on the hysteresis 100p and the structure are also investigated. The last part is to simuiate electro-thermo-convection when injection and thermal gradient are simultaneously applied. Our solver was verified with a stationary and an overstable stability problem.The case that both heating and injection are from a bottom electrode has been analyzed in details. The neutral stabiliïy curve was reproduced. The existence of nonlinear phenornenon and the structure are highlighted.

As a first aspect the thesis treats existence results of the perturbed eigenvalue problem of the 1-Laplace operator. This is done with the aid of a quite general critical point theory results with the genus as topological index. Moreover we show that the eigenvalues of the perturbed 1-Laplace operator converge to the eigenvalues of the unperturebed 1-Laplace operator when the perturbation goes to zero. As a second aspect we treat the eigenvalue problems of the vectorial 1-Laplace operator and the symmetrized 1-Laplace operator. And as a third aspect certain related parabolic problems are considered.

In this thesis we study novel higher order total variation-based variational methods for digital image reconstruction. These methods are formulated in the context of Tikhonov regularisation. We focus on regularisation techniques in which the regulariser incorporates second order derivatives or a sophisticated combination of first and second order derivatives. The introduction of higher order derivatives in the regularisation process has been shown to be an advantage over the classical first order case, i.e., total variation regularisation, as classical artifacts such as the staircasing effect are significantly reduced or totally eliminated. Also in image inpainting the introduction of higher order derivatives in the regulariser turns out to be crucial to achieve interpolation across large gaps. First, we introduce, analyse and implement a combined first and second order regularisation method with applications in image denoising, deblurring and inpainting. The method, numerically realised by the split Bregman algorithm, is computationally efficient and capable of giving comparable results with total generalised variation (TGV), a state of the art higher order method. An additional experimental analysis is performed for image inpainting and an online demo is provided on the IPOL website (Image Processing Online). We also compute and study properties of exact solutions of the one dimensional total generalised variation problem with L^{2} data fitting term, for simple piecewise affine data functions, with or without jumps . This gives an insight on how this type of regularisation behaves and unravels the role of the TGV parameters. Finally, we introduce, study and analyse a novel non-local Hessian functional. We prove localisations of the non-local Hessian to the local analogue in several topologies and our analysis results in derivative-free characterisations of higher order Sobolev and BV spaces. An alternative formulation of a non-local Hessian functional is also introduced which is able to produce piecewise affine reconstructions in image denoising, outperforming TGV.

L’entropie d’une distribution sur un ensemble de variables aléatoires discrètes est toujours bornée par l’entropie de la distribution factorisée correspondante. Cette propriété est due à la sous-modularité de l’entropie. Par ailleurs, les fonctions sous-modulaires sont une généralisation des fonctions de rang des matroïdes ; ainsi, les fonctions linéaires sur les polytopes associés peuvent être minimisées exactement par un algorithme glouton. Dans ce manuscrit, nous exploitons ces liens entre les structures des modèles graphiques et les fonctions sous-modulaires. Nous utilisons des algorithmes gloutons pour optimiser des fonctions linéaires sur des polytopes liés aux matroïdes graphiques et hypergraphiques pour apprendre la structure de modèles graphiques, tandis que nous utilisons des algorithmes d’inférence sur les graphes pour optimiser des fonctions sous-modulaires. La première contribution de cette thèse consiste à approcher par maximum de vraisemblance une distribution de probabilité par une distribution factorisable et de complexité algorithmique contrôlée. Comme cette complexité est exponentielle dans la largeur arborescente du graphe, notre but est d’apprendre un graphe décomposable avec une largeur arborescente bornée, ce qui est connu pour être NP-difficile. Nous posons ce problème comme un problème d’optimisation combinatoire et nous proposons une relaxation convexe basée sur les matroïdes graphiques et hypergraphiques. Ceci donne lieu à une solution approchée avec une bonne performance pratique. Pour la seconde contribution principale, nous utilisons le fait que l’entropie d’une distribution est toujours bornée par l’entropie de sa distribution factorisée associée, comme conséquence principale de la sous-modularité, permettant une généralisation à toutes les fonctions sous-modulaires de bornes basées sur les concepts de modèles graphiques. Un algorithme est développé pour maximiser les fonctions sous-modulaires, un autre problème NP-difficile, en maximisant ces bornes en utilisant des algorithmes d’inférence vibrationnels sur les graphes. En troisième contribution, nous proposons et analysons des algorithmes visant à minimiser des fonctions sous-modulaires pouvant s’écrire comme somme de fonctions plus simples. Nos algorithmes n’utilisent que des oracles de ces fonctions simple basés sur minimisation sous-modulaires et de variation totale de telle fonctions. / The entropy of a probability distribution on a set of discrete random variables is always bounded by the entropy of its factorisable counterpart. This is due to the submodularity of entropy on the set of discrete random variables. Submodular functions are also generalisation of matroid rank function; therefore, linear functions may be optimised on the associated polytopes exactly using a greedy algorithm. In this manuscript, we exploit these links between the structures of graphical models and submodular functions: we use greedy algorithms to optimise linear functions on the polytopes related to graphic and hypergraphic matroids for learning the structures of graphical models, while we use inference algorithms on graphs to optimise submodular functions.The first main contribution of the thesis aims at approximating a probabilistic distribution with a factorisable tractable distribution under the maximum likelihood framework. Since the tractability of exact inference is exponential in the treewidth of the decomposable graph, our goal is to learn bounded treewidth decomposable graphs, which is known to be NP-hard. We pose this as a combinatorial optimisation problem and provide convex relaxations based on graphic and hypergraphic matroids. This leads to an approximate solution with good empirical performance. In the second main contribution, we use the fact that the entropy of a probability distribution is always bounded by the entropy of its factorisable counterpart mainly as a consequence of submodularity. This property of entropy is generalised to all submodular functions and bounds based on graphical models are proposed. We refer to them as graph-based bounds. An algorithm is developped to maximise submodular functions, which is NPhard, by maximising the graph-based bound using variational inference algorithms on graphs. As third contribution, we propose and analyse algorithms aiming at minimizing submodular functions that can be written as sum of simple functions. Our algorithms only make use of submodular function minimisation and total variation oracles of simple functions.

Slepá dekonvoluce je metoda, při které je rozptylová funkce a skutečný obraz rekonstruován zároveň. Cílem této práce je představit různé metody slepé dekonvoluce a najít optimální metodu rekonstrukce původního obrazu a rozptylové funkce. Jako nejvhodnější metoda slepé dekonvoluce byl zvolen algoritmus střídavé minimalizace, který byl upraven a testován. Vlastnosti navrženého algoritmu byly testovány na uměle degradovaných datech a na reálných datech pořízených skenovacím transmisním elektronovým mikroskopem. Účinnost algoritmu byla hodnocena hned několika hodnotícími kritérii. Byla zjištěna omezení algoritmu a tím specifikováno jeho využití.

Tato práce se zabývá doplňováním chybějících dat do audio signálů a algoritmy řešícími problém založenými na řídké reprezentaci audio signálu. Práce se zaměřuje na některé algoritmy, které řeší doplňování chybějících dat do audio signálů pomocí řídké reprezentace signálů. Součástí práce je také návrh algoritmu, který používá řídkou reprezentaci signálu a také nízkou hodnost signálu ve spektrogramu audio signálu. Dále práce uvádí implementaci tohoto algoritmu v programu Matlab a jeho vyhodnocení.

This Master Thesis deals with image restoration using deconvolution. The terms introducing into deconvolution theory like two-dimensional signal, distortion model, noise and convolution are explained in the first part of thesis. The second part deals with deconvolution methods via utilization of the Bayes approach which is based on the probability principle. The third part is focused on the Alternating Minimization Algorithm for Multichannel Blind Deconvolution. At the end this algorithm is written in Matlab with utilization of the NAG C Library. Then comparison of different optimization methods follows (simplex, steepest descent, quasi-Newton), regularization forms (Tichonov, Total Variation) and other parameters used by this deconvolution algorithm.