La motilité cellulaire est un processus intégré essentiel à de nombreux phénomènes physiologiques tels que la formation du cône de croissance et la plasticité synaptique. Des dérégulations de la motilité cellulaire peuvent être à l’origine de la formation de métastases ou de pathologies neuropsychiatriques comme la schizophrénie et l'autisme. La compréhension des mécanismes régulant la migration cellulaire est donc un enjeu majeur. La motilité cellulaire repose sur la formation de diverses structures constituées de réseaux d’actine branchés telles que le lamellipode. La formation du lamellipode nécessite l’intervention de protéines régulatrices de l’actine telles que Rac1 et les complexes Wave et Arp2/3. Grâce à l’utilisation de suivi de protéine unique, nous avons pu comprendre comment la coordination spatio-temporelle de ces régulateurs contrôle la formation et la morphologie des lamellipodes de cellules migrantes. Nous avons ainsi découvert que l’activation et la localisation du complexe Wave étaient régulées de manière enzymatique mais également mécanique. Dans une première étude, nous avons montré que la RhoGTPase Rac1 active le complexe Wave spécifiquement à l’extrémité du lamellipode. Dans une seconde étude, nous avons révélé que la localisation du complexe Wave est régulée par la dynamique des filaments des réseaux branchés d’actine. Ces données soulignent l’importance du complexe Wave dans la formation du lamellipode et révèlent l’existence d’une régulation mécanique de la localisation du complexe Wave. / Cell motility is an integrated process involved in critical phenomena such as axonal pathfinding and synaptic plasticity. Dysregulation of cell motility can induce metastasis and abnormal spine shapes observed in neuropsychiatric disorders like autism and schizophrenia. Therefore it is essential to understand how cell motility is regulated. Cell motility requires the formation of branched actin networks propelled by actin polymerization that lead to the formation of membrane protrusions such as the lamellipodium. Several actin regulatory proteins are involved in this process, such as Rac1 and the WAVE and ARP2/3 complexes. Using single protein tracking, we revealed key phenomena concerning the spatio-temporal regulation of lamellipodium formation by actin regulatory proteins. We found that the localization and activation of the WAVE complex was enzymatically regulated, but also mechanically. First, we showed that the Rac1 RhoGTPase activates the WAVE complex specifically at the tip of the lamellipodium. We also showed that WAVE complex localization is regulated by the dynamics of branched-network actin filaments. This study confirms the crucial role of the WAVE complex in lamellipodium formation and reveals the existence of a mechanical regulation of the localization of this complex in the cell.

Nous avons développé un microscope tomographique diffractif en réflexion, qui permet d’observer la surface d’un échantillon avec une résolution latérale améliorée comparée à un microscope holographique conventionnel. À partir des même données expérimentales (les hologrammes acquis sous différents angles d’illumination), des mesures à haute précision longitudinale peuvent être réalisées sur la surface d’un échantillon purement réfléchissant, par reconstruction du profil de hauteur à partir de la phase. Cette méthode d’imagerie multimodale présente plusieurs avantages comparée aux mesures en holographie interférométrique classique : amélioration de la résolution latérale sur la partie diffractive, déroulement de phase facilité, réduction du bruit cohérent, l’ensemble étant associé à la grande précision longitudinale fournie par les mesures de phase. Nous montrons ces possibilités en imageant divers échantillons minces. / We have developed a tomographic diffractive microscope in reflection, which permits observation of sample surfaces with an improved lateral resolution, compared to a conventional holographic microscope. From the same set of data, high-precision measurements can be performed on the shape of the reflective surface by reconstructing the phase of the diffracted field. doing so allows for several advantages compared to classical holographic interferometric measurements: improvement in lateral resolution, easier phase unwrapping, reduction of the coherent noise, combined with the high-longitudinal precision provided by interferometric phase measurements. We demonstrate these capabilities by imaging various test samples.

This thesis presents new approaches to image registration and super-resolution mosaicing as well as their applications. Firstly, a feature-based image registration method is proposed for a multisensor surveillance system that consists of an optical camera and an infrared camera. By integrating a non-rigid object tracking technique into this method, a novel approach to simultaneous object tracking and multisensor image registration is proposed. Based on the registration and fusion of multisensor information, automatic face detection is greatly improved. Secondly, some extensions of a gradient-based image registration method, called inverse compositional algorithm, are proposed. These extensions include cumulative multi-image registration and the incorporation of illumination change and lens distortion correction. They are incorporated into the framework of the original algorithm in a consistent manner and efficiency can still be achieved for multi-image registration with illumination and lens distortion correction. Thirdly, new super-resolution mosaicing algorithms are proposed for multiple uncompressed and compressed images. Considering the process of image formation, observation models are introduced to describe the relationship between the superresolution mosaic image and the uncompressed and compressed low-resolution images. To improve the performance of super-resolution mosaicing, a wavelet-based image interpolation technique and an approach to adaptive determination of the regularization parameter are presented. For compressed images, a spatial-domain algorithm and a transform-domain algorithm are proposed. All the proposed superresolution mosaicing algorithms are robust against outliers. They can produce superresolution mosaics and reconstructed super-resolution images with improved subjective quality. Finally, new techniques for super-resolution sprite generation and super-resolution sprite coding are proposed. Considering both short-term and long-term motion influences, an object-based image registration method is proposed for handling long image sequences. In order to remove the influence of outliers, a robust technique for super-resolution sprite generation is presented. This technique produces sprite images and reconstructed super-resolution images with high visual quality. Moreover, it provides better reconstructed low-resolution images compared with low-resolution sprite generation techniques. Due to the advantages of the super-resolution sprite, a super-resolution sprite coding technique is also proposed. It achieves high coding efficiency especially at a low bit-rate and produces both decoded low-resolution and super-resolution images with improved subjective quality. Throughout this work, the performance of all the proposed algorithms is evaluated using both synthetic and real image sequences.

At present, the quality of image taken from infrared cameras is low compared to the other cameras because of manufacturing technology. So, resolution enhancement processes are becoming more important for these cameras. Super resolution is a good approach to solve this resolution problem. In general, the systems that infrared cameras used require video processing to perform in real time. So, a suitable approach should be selected and implemented to work in real time. The computational load and processing time are big issues in this case. FPGAs are proven to be suitable hardware devices for these types of works. Super resolution involves two parts as global motion estimation and high resolution image reconstruction. In this study, one suitable algorithm, namely as PM, for global motion estimation in the literature is selected to be implemented in real time. On the other hand, for high resolution image reconstruction part, FPGA structures of some well known algorithms in the literature, namely as POCS, MLE, MAP and LMS are proposed and their performance, resource requirements and timing considerations are discussed. Most efficient one is selected and implemented in FPGA.

In this thesis, it is proposed to get the high definition color images by using super &ndash / resolution algorithms. Resolution enhancement of RGB, HSV and YIQ color domain images is presented. In this study, three solution methods are presented to improve the resolution of HSV color domain images. These solution methods are suggested to beat the color artifacts on super resolution image and decrease the computational complexity in HSV domain applications. PSNR values are measured and compared with the results of other two color domain experiments. In RGB color space, super &ndash / resolution algorithms are applied three color channels (R, G, B) separately and PSNR values are measured. In YIQ color domain, only Y channel is processed with super resolution algorithms because Y channel is luminance component of the image and it is the most important channel to improve the resolution of the image in YIQ color domain. Also, the third solution method suggested for HSV color domain offers applying super resolution algorithm to only value channel. Hence, value channel carry brightness data of the image. The results are compared with the YIQ color domain experiments. During the experiments, four different super resolution algorithms are used that are Direct Addition, MAP, POCS and IBP. Although, these methods are widely used reconstruction of monochrome images, here they are used for resolution enhancement of color images. Color super resolution performances of these algorithms are tested.

Magnetic resonance imaging (MRI) is the preferred imaging modality for visualization of intracranial soft tissues. Surgical planning, and increasingly surgical navigation, use high resolution 3-D patient-specific structural maps of the brain. However, the process of MRI is a multi-parameter tomographic technique where high resolution imagery competes against high contrast and reasonable acquisition times. Resolution enhancement techniques based on super-resolution are particularly well suited in solving the problems of resolution when high contrast with reasonable times for MRI acquisitions are needed. Super-resolution is the concept of reconstructing a high resolution image from a set of low-resolution images taken at dierent viewpoints or foci. The MRI encoding techniques that produce high resolution imagery are often sub-optimal for the desired contrast needed for visualization of some structures in the brain. A novel super-resolution reconstruction framework for MRI is proposed in this thesis. Its purpose is to produce images of both high resolution and high contrast desirable for image-guided minimally invasive brain surgery. The input data are multiple 2-D multi-slice Inversion Recovery MRI scans acquired at orientations with regular angular spacing rotated around a common axis. Inspired by the computed tomography domain, the reconstruction is a 3-D volume of isotropic high resolution, where the inversion process resembles a projection reconstruction problem. Iterative algorithms for reconstruction are based on the projection onto convex sets formalism. Results demonstrate resolution enhancement in simulated phantom studies, and in ex- and in-vivo human brain scans, carried out on clinical scanners. In addition, a novel motion correction method is applied to volume registration using an iterative technique in which super-resolution reconstruction is estimated in a given iteration following motion correction in the preceding iteration. A comparison study of our method with previously published methods in super-resolution shows favorable characteristics of the proposed approach.

The Kalman Filter has many applications in control and signal processing but may also be used to reconstruct a higher resolution image from a sequence of lower resolution images (or frames). If the sequence of low resolution frames is recorded by a moving camera or sensor, where the motion can be accurately modeled, then the Kalman filter may be used to update pixels within a higher resolution frame to achieve a more detailed result. This thesis outlines current methods of implementing this algorithm on a scene of interest and introduces possible improvements for the speed and efficiency of this method by use of block operations on the low resolution frames. The effects of noise on camera motion and various blur models are examined using experimental data to illustrate the differences between the methods discussed.

Subject identification from surveillance imagery has become an important task for forensic investigation. Good quality images of the subjects are essential for the surveillance footage to be useful. However, surveillance videos are of low resolution due to data storage requirements. In addition, subjects typically occupy a small portion of a camera's field of view. Faces, which are of primary interest, occupy an even smaller array of pixels. For reliable face recognition from surveillance video, there is a need to generate higher resolution images of the subject's face from low-resolution video. Super-resolution image reconstruction is a signal processing based approach that aims to reconstruct a high-resolution image by combining a number of low-resolution images. The low-resolution images that differ by a sub-pixel shift contain complementary information as they are different "snapshots" of the same scene. Once geometrically registered onto a common high-resolution grid, they can be merged into a single image with higher resolution. As super-resolution is a computationally intensive process, traditional reconstruction-based super-resolution methods simplify the problem by restricting the correspondence between low-resolution frames to global motion such as translational and affine transformation. Surveillance footage however, consists of independently moving non-rigid objects such as faces. Applying global registration methods result in registration errors that lead to artefacts that adversely affect recognition. The human face also presents additional problems such as selfocclusion and reflectance variation that even local registration methods find difficult to model. In this dissertation, a robust optical flow-based super-resolution technique was proposed to overcome these difficulties. Real surveillance footage and the Terrascope database were used to compare the reconstruction quality of the proposed method against interpolation and existing super-resolution algorithms. Results show that the proposed robust optical flow-based method consistently produced more accurate reconstructions. This dissertation also outlines a systematic investigation of how super-resolution affects automatic face recognition algorithms with an emphasis on comparing reconstruction- and learning-based super-resolution approaches. While reconstruction-based super-resolution approaches like the proposed method attempt to recover the aliased high frequency information, learning-based methods synthesise them instead. Learning-based methods are able to synthesise plausible high frequency detail at high magnification ratios but the appearance of the face may change to the extent that the person no longer looks like him/herself. Although super-resolution has been applied to facial imagery, very little has been reported elsewhere on measuring the performance changes from super-resolved images. Intuitively, super-resolution improves image fidelity, and hence should improve the ability to distinguish between faces and consequently automatic face recognition accuracy. This is the first study to comprehensively investigate the effect of super-resolution on face recognition. Since super-resolution is a computationally intensive process it is important to understand the benefits in relation to the trade-off in computations. A framework for testing face recognition algorithms with multi-resolution images was proposed, using the XM2VTS database as a sample implementation. Results show that super-resolution offers a small improvement over bilinear interpolation in recognition performance in the absence of noise and that super-resolution is more beneficial when the input images are noisy since noise is attenuated during the frame fusion process.

Over the past three decades in cellular neuroscience there has been a shift towards the view of the 'tripartite synapse', where, astrocytes -- as well as the pre-synapse and post-synapse -- are involved in synaptic signalling. The migration of astrocytes to form branched networks in the brain is, therefore, of great interest in understanding brain development and neuronal function. Migration is a complex interplay between cytoskeletal reorganisation and cell mechanical stiffness. In order to improve understanding of this process, correlative measurements of cytoskeletal organisation and mechanical stiffness are required. To investigate astrocyte migration a technique combining atomic force microscopy (AFM) with stimulated emission depletion (STED) microscopy was developed. First a custom STED microscope was developed. To facilitate the design of this system the theoretical performance of a range of STED techniques (cw-STED, time-gated STED, pulsed STED and RESOLFT) were compared, identifying that pulsed STED theoretically has the highest photon efficiency. A pulsed STED microscope, which uses adaptive optics, was then designed, developed and characterised. The microscope was found to achieve resolutions below 50 nm. The STED microscope was combined with a commercial AFM to study live cells. Using the recently developed SiR-actin and SiR-tubulin dyes and AFM probes optimised for live cell mechanical property studies, images of the actin and tubulin cytoskeleton were correlated with AFM topography and mechanical stiffness measurements. It was found that, in astrocytes, actin contributes significantly both to astrocyte stiffness and topography. Investigations of migrating cells showed differences in actin organisation and mechanical stiffness between the basis and leading edge of migration. A further study was performed, investigating the effects of the gap-junction protein connexin30, which is expressed during the early stages of brain development, on migration. This protein was found to inhibit the actin reorganisation and mechanical stiffness changes observed in basal conditions. Overall the combination of mechanosensitive AFM measurements with advanced microscopy, such as super-resolution, on live cells is a promising approach which will enable a range of investigations, for instance when studying cell structural remodeling during brain development or tumorigenesis.

La division bactérienne résulte de la constriction de la membrane, menée par la protéine du cytosquelette FtsZ, et de l’expansion et du remodelage de la paroi, réalisés par des synthétases et des hydrolases de la paroi. La coordination de ces processus au sein d’un macrocomplexe protéique, le divisome, est nécessaire au maintien de la forme et de l’intégrité bactérienne. J’ai étudié deux aspects importants de ce mécanisme de coordination chez le pathogène humain Streptococcus pneumoniae. J’ai déterminé in vivo la nanostructure de la protéine FtsZ en développant l’utilisation du PALM (PhotoActivated Localization Microscopy)chez le pneumocoque. Cette technique, basée sur la détection de molécules uniques et permettant une résolution de 20-40 nm, a révélé des aspects inattendus (dimensions, amas, sous-structures) de l’architecture de l’anneau de FtsZ au cours du cycle cellulaire. En parallèle, j’ai étudié le rôle de l’hydrolase Pmp23 par génétique, biochimie et microscopie à fluorescence. Mon travail a montré que Pmp23 est requise pour la stabilité des macrostructures du divisome du pneumocoque, révélant une nouvelle connexion entre le métabolisme de la paroi et la division cellulaire. / Bacterial division results from the combination of membrane constriction, driven by the cytoskeletal protein FtsZ, with cell wall expansion and remodeling, performed by cell wall synthases and hydrolases. Coordination of these processes within a large protein complex known as the divisome ensures cell integrity and maintenance of cell shape. I have investigated two important aspects of this coordination mechanism in the human pathogen Streptococcus pneumoniae. I determined the in vivo nanostructure of the divisome scaffolding protein FtsZ by developing the use of PhotoActivated Localization Microscopy (PALM) in the pneumococcus. PALM, which is based on the detection of single fluorescent labels and allows 20-40 nm resolution, has revealed unexpected features (dimensions, clusters, new substructures) of the FtsZ-ring architecture along the cell cycle. In parallel, I studied the role of the cell wall hydrolase Pmp23 using genetics, biochemistry and fluorescence microscopy. My work has shown that Pmp23 is required for the stability of divisome macrostructures in the pneumococcal cell, revealing a new connection between cell wall metabolism and cell division.