Cette thèse traite de problèmes liés à la couleur. En particulier, on s’intéresse à des problématiques communes à la colorisation d’images, de vidéos et au rehaussement de contraste. Si on considère qu’une image est composée de deux informations complémentaires, une achromatique (sans couleur) et l’autre chromatique (en couleur), les applications étudiées consistent à traiter une de ces deux informations en préservant sa complémentaire. En colorisation, la difficulté est de calculer une image couleur en imposant son niveau de gris. Le rehaussement de contraste vise à modifier l’intensité d’une image en préservant sa teinte. Ces problématiques communes nous ont conduits à étudier formellement la géométrie de l’espace RGB. On a démontré que les espaces couleur classiques de la littérature pour résoudre ces types de problème conduisent à des erreurs. Un algorithme, appelé spécification luminance-teinte, qui calcule une couleur ayant une teinte et une luminance données est décrit dans cette thèse. L’extension de cette méthode à un cadre variationnel a été proposée. Ce modèle a été utilisé avec succès pour rehausser les images couleur, en utilisant des hypothèses connues sur le système visuel humain. Les méthodes de l’état-de-l’art pour la colorisation d’images se divisent en deux catégories. La première catégorie regroupe celles qui diffusent des points de couleurs posés par l’utilisateur pour obtenir une image colorisée (colorisation manuelle). La seconde est constituée de celles qui utilisent une image couleur de référence ou une base d’images couleur et transfèrent les couleurs de la référence sur l’image en niveaux de gris (colorisation basée exemple). Les deux types de méthodes ont leurs avantages et inconvénients. Dans cette thèse, on propose un modèle variationnel pour la colorisation basée exemple. Celui-ci est étendu en une méthode unifiant la colorisation manuelle et basée exemple. Enfin, nous décrivons des modèles variationnels qui colorisent des vidéos tout en permettent une interaction avec l’utilisateur. / This thesis deals with problems related to color. In particular, we are interested inproblems which arise in image and video colorization and contrast enhancement. When considering color images composed of two complementary information, oneachromatic (without color) and the other chromatic (in color), the applications studied in this thesis are based on the processing one of these information while preserving its complement. In colorization, the challenge is to compute a color image while constraining its gray-scale channel. Contrast enhancement aims to modify the intensity channel of an image while preserving its hue.These joined problems require to formally study the RGB space geometry. In this work, it has been shown that the classical color spaces of the literature designed to solve these classes of problems lead to errors. An novel algorithm, called luminance-hue specification, which computes a color with a given hue and luminance is described in this thesis. The extension of this method to a variational framework has been proposed. This model has been used successfully to enhance color images, using well-known assumptions about the human visual system. The state-of-the-art methods for image colorization fall into two categories. The first category includes those that diffuse color scribbles drawn by the user (manual colorization). The second consists of those that benefits from a reference color image or a base of reference images to transfer the colors from the reference to the grayscale image (exemplar-based colorization). Both approach have their advantages and drawbacks. In this thesis, we design a variational model for exemplar-based colorization which is extended to a method unifying the manual colorization and the exemplar-based one. Finally, we describe two variational models to colorize videos in interaction with the user.

The wavelet transform has been used for several decades and it is still an object of research - especially its recent modifications which are using the so-called second generation wavelets. It has several advantages over other integral transformations. The most important of them are the ability to localize both in time and frequency and an ability to decorrelate some real non-stationary signals such as images. For this reasons, the wavelet transform became an often used tool in many image processing tasks, for example in image compression, edge detection or contrast enhancement. In this thesis, the wavelet transform is explained, including the theoretical foundation and implementation for use with two-dimensional discrete signals. Some of the applications of the wavelet transform are presented and described. The wavelet transform showed to be suitable tool for edge detection, noise reduction, contrast enhancement and HDR compression.

Ultrasound imaging is widely used in medical examination. However, the interpretation of images is not trivial and requires much experience. In this thesis, various techniques for enhancement of visual quality of ultrasound images are presented. Several basic and advanced methods that may simplify the visual diagnosis are described. Finally, an interactive application is designed and implemented for simple usage of presented methods.

<p dir="ltr">Manganese (Mn) is an essential trace element for humans that functions primarily as a coenzyme in several biological processes such as nerve and brain development, energy metabolism, bone growth and development, as well as cognitive functioning. However, overexposure to environmental Mn via occupational settings or contaminated drinking water can lead to toxic effects on the central nervous systems and cause a Parkinsonian disorder that features symptoms such as fine motor control deficits, dystonia rigidity, speech and mood disturbances, and cognitive deficits summarized under the term “manganism”. Over time, Mn exposure has shifted from acute, high-level instances leading to manganism, to low-level chronic exposure. Considering that Mn exposure is significantly lower than in the past, it is unlikely to expect manganism from chronic Mn exposure under current working conditions. Therefore, there is a need to develop sensitive methods to aid in updating the clinical diagnostic standards for manganism and Mn neurotoxicity as chronic exposure to Mn leads to more subtle symptoms.</p><p><br></p><p dir="ltr">Historically, magnetic resonance imaging (MRI) has been used as a non-invasive tool for detecting excess brain Mn accumulation. Specifically, T1-weighted images show bilateral hyperintensities of the globus pallidus (GP) due to the paramagnetic properties of Mn which increases the MR relaxation rate R1. Although the GP is considered the hallmark of excess brain Mn, this brain area is not necessarily associated with symptoms, exposure, or neuropsychological outcomes. Thus, the focus should not be on the GP only but on the entire brain. With recent advances in quantitative MRI (qMRI), whole brain mapping techniques allow for the direct measurement of relaxation rate changes due to Mn accumulation. The work in this dissertation uses such quantitative techniques and network science to establish novel computational in vivo imaging methods to a) visualize and quantify excess Mn deposition at the group and individual level, and b) characterize the toxicokinetics of excess brain Mn accumulation and the role of different brain regions in the development of neurotoxicity effects.</p><p><br></p><p dir="ltr">First, we developed a novel method for depicting excess Mn accumulation at the group level using high-resolution R1 relaxation maps to identify regional differences using voxel-based quantification (VBQ) and statistical parametric mapping. Second, we departed from a group analysis and developed subject-specific maps of excess brain Mn to gain a better understanding of the relationship between the spatial distribution of Mn and exposure settings. Third, we developed a novel method that combines network science with MRI relaxometry to characterize the storage and propagation of Mn and Fe in the human brain and the role of different brain regions in the development of neurotoxic effects. Lastly, we explore the application of ultra-short echo (UTE) imaging to map Fe content in the brain and compare it against R2* and quantitative susceptibility mapping (QSM).</p><p><br></p><p dir="ltr">Overall, this dissertation is a successful step towards establishing sensitive neuroimaging screening methods to study the effects of occupational Mn exposure. The individual Mn maps offer great potential for evaluating personal risk assessment for Mn neurotoxicity and allow monitoring of temporal changes in an individual, offering valuable information about the toxicokinetics of Mn. The integration of network science provides a holistic analysis to identify subtle changes in the brain’s mediation mechanisms of excess metal depositions and their associations with health outcomes.</p>

Dans cette thèse CIFRE en collaboration entre le Centre de Mathématiques et de leurs Applications, École Normale Supérieure de Cachan et l’entreprise DxO, nous abordons le problème de la décomposition additive d’une image en base et détail. Une telle décomposition est un outil fondamental du traitement d’image. Pour une application à la photographie professionnelle dans le logiciel DxO Photolab, il est nécessaire que la décomposition soit exempt d’artefact. Par exemple, dans le contexte de l’amélioration de contraste, où la base est réduite et le détail augmenté, le moindre artefact devient fortement visible. Les distorsions de l’image ainsi introduites sont inacceptables du point de vue d’un photographe.L’objectif de cette thèse est de trouver et d’étudier les filtres les plus adaptés pour effectuer cette tâche, d’améliorer les meilleurs et d’en définir de nouveaux. Cela demande une mesure rigoureuse de la qualité de la décomposition en base plus détail. Nous examinons deux artefact classiques (halo et staircasing) et en découvrons trois autres types tout autant cruciaux : les halos de contraste, le cloisonnement et les halos sombres. Cela nous conduit à construire cinq mire adaptées pour mesurer ces artefacts. Nous finissons par classer les filtres optimaux selon ces mesures, et arrivons à une décision claire sur les meilleurs filtres. Deux filtres sortent du rang, dont un proposé dans cette thèse. / In this CIFRE thesis, a collaboration between the Center of Mathematics and their Applications, École Normale Supérieure de Cachan and the company DxO, we tackle the problem of the additive decomposition of an image into base and detail. Such a decomposition is a fundamental tool in image processing. For applications to professional photo editing in DxO Photolab, a core requirement is the absence of artifacts. For instance, in the context of contrast enhancement, in which the base is reduced and the detail increased, minor artifacts becomes highly visible. The distortions thus introduced are unacceptable from the point of view of a photographer.The objective of this thesis is to single out and study the most suitable filters to perform this task, to improve the best ones and to define new ones. This requires a rigorous measure of the quality of the base plus detail decomposition. We examine two classic artifacts (halo and staircasing) and discover three more sorts that are equally crucial: contrast halo, compartmentalization, and the dark halo. This leads us to construct five adapted patterns to measure these artifacts. We end up ranking the optimal filters based on these measurements, and arrive at a clear decision about the best filters. Two filters stand out, including one we propose.