Isotropic inverse-problem approach for two-dimensional phase unwrapping

In this paper, we propose a new technique for two-dimensional phase unwrapping. The unwrapped phase is found as the solution of an inverse problem that consists in the minimization of an energy functional. The latter includes a weighted data-fidelity term that favors sparsity in the error between the true and wrapped phase differences, as well as a regularizer based on higher-order total-variation. One desirable feature of our method is its rotation invariance, which allows it to unwrap a much larger class of images compared to the state of the art. We demonstrate the effectiveness of our method through several experiments on simulated and real data obtained through the tomographic phase microscope. The proposed method can enhance the applicability and outreach of techniques that rely on quantitative phase evaluation

Vision Sensors and Edge Detection

Vision Sensors and Edge Detection book reflects a selection of recent developments within the area of vision sensors and edge detection. There are two sections in this book. The first section presents vision sensors with applications to panoramic vision sensors, wireless vision sensors, and automated vision sensor inspection, and the second one shows image processing techniques, such as, image measurements, image transformations, filtering, and parallel computing

Synthetic Aperture Radar (SAR) Meets Deep Learning

This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

Advanced Image Acquisition, Processing Techniques and Applications

"Advanced Image Acquisition, Processing Techniques and Applications" is the first book of a series that provides image processing principles and practical software implementation on a broad range of applications. The book integrates material from leading researchers on Applied Digital Image Acquisition and Processing. An important feature of the book is its emphasis on software tools and scientific computing in order to enhance results and arrive at problem solution

Resolving Measurement Errors Inherent with Time-of-Flight Range Imaging Cameras

Range imaging cameras measure the distance to objects in the field-of-view (FoV) of the camera, these cameras enable new machine vision applications in robotics, manufacturing, and human computer interaction. Time-of-flight (ToF) range cameras operate by illuminating the scene with amplitude modulated continuous wave (AMCW) light and measuring the phase difference between the emitted and reflected modulation envelope. Currently ToF range cameras suffer from measurement errors that are highly scene dependent, and these errors limit the accuracy of the depth measurement. The major cause of measurement errors is multiple propagation paths from the light source to pixel, known as multi path interference. Multi-path interference typically arises from: inter reflections, lens flare, subsurface scattering, volumetric scattering, and translucent objects. This thesis contributes three novel methods for resolving multi-path interference: coding in time, coding in frequency, and coding in space. Time coding is implemented by replacing the single frequency amplitude modulation with a binary sequence. Fundamental to ToF range cameras is the cross-correlation between the reflected light and a reference signal. The measured cross-correlation depends on the selection of the binary sequence. With selection of an appropriate binary sequence and using sparse deconvolution on the measured cross-correlation the multiple return path lengths and their amplitudes can be recovered. However, the minimal resolvable path length is dependent on the highest frequency in the binary sequence. Frequency coding is implemented by taking multiple measurements at different modulation frequencies. A subset of frequency coding is operating the camera in a mode analogous to stepped frequency continuous wave (SFCW). Frequency coding uses techniques from radar to resolve multiple propagation paths. The minimal resolvable path length is dependent on the camera's modulation bandwidth and the spectrum estimation technique used to recover distance, and it is shown that SFCW can be used to measure depth of objects behind a translucent sheet, while AMCW measurements can not. Path lengths below quarter a wavelength of the highest modulation frequency are difficult to resolve. The use of spatial coding is used to resolve diffuse multi-path interference. The original technique comes from direct and global separation in computer graphics, and it is modified to operate on the complex data produced by a ToF range camera. By illuminating the scene with a pattern the illuminated areas contain the direct return and the scattering (global return). The non-illuminated regions contain the scattering return, assuming the global component is spatially smooth. The direct and global separation with sinusoidal patterns is combining with the sinusoidal modulation signal of ToF range cameras for a closed form solution to multi-path interference in nine frames. With nine raw frames it is possible to implement direct and global separation at video frame rates. The RMSE of a corner is reduced from 0.0952 m to 0.0112 m. Direct and global separation correctly measures the depth of a diffuse corner, and resolves subsurface scattering however fails to resolve specular reflections. Finally the direct and global separation is combined with replacing the illumination and reference signals with a binary sequence. The combination allows for resolving diffuse multi-path interference present in a corner, with the sparse multi-path interference caused mixed pixels between the foreground and background. The corner is correctly measured and the number of mixed pixels is reduced by 90%. With the development of new methods to resolve multi-path interference ToF range cameras can measure scenes with more confidence. ToF range cameras can be built into small form factors as they require a small number of parts: a pixel array, a light source and a lens. The small form factor coupled with accurate range measurements allows ToF range cameras to be embedded in cellphones and consumer electronic devices, enabling wider adoption and advantages over competing range imaging technologies

Fundamental and Harmonic Ultrasound Image Joint Restoration

L'imagerie ultrasonore conserve sa place parmi les principales modalités d'imagerie en raison de ses capacités à révéler l'anatomie et à inspecter le mouvement des organes et le flux sanguin en temps réel, d'un manière non invasive et non ionisante, avec un faible coût, une facilité d'utilisation et une grande vitesse de reconstruction des images. Néanmoins, l'imagerie ultrasonore présente des limites intrinsèques en termes de résolution spatiale. L'amélioration de la résolution spatiale des images ultrasonores est un défi actuel et de nombreux travaux ont longtemps porté sur l'optimisation du dispositif d'acquisition. L'imagerie ultrasonore à haute résolution atteint cet objectif grâce à l'utilisation de sondes spécialisées, mais se confronte aujourd'hui à des limites physiques et technologiques. L'imagerie harmonique est la solution intuitive des spécialistes pour augmenter la résolution lors de l'acquisition. Cependant, elle souffre d'une atténuation en profondeur. Une solution alternative pour améliorer la résolution est de développer des techniques de post-traitement comme la restauration d'images ultrasonores. L'objectif de cette thèse est d'étudier la non-linéarité des échos ultrasonores dans le processus de restauration et de présenter l'intérêt d'incorporer des images US harmoniques dans ce processus. Par conséquent, nous présentons une nouvelle méthode de restauration d'images US qui utilise les composantes fondamentales et harmoniques de l'image observée. La plupart des méthodes existantes sont basées sur un modèle linéaire de formation d'image. Sous l'approximation de Born du premier ordre, l'image RF est supposée être une convolution 2D entre la fonction de réflectivité et la réponse impulsionelle du système. Par conséquent, un problème inverse résultant est formé et résolu en utilisant un algorithme de type ADMM. Plus précisément, nous proposons de récupérer la fonction de reflectivité inconnue en minimisant une fonction composée de deux termes de fidélité des données correspondant aux composantes linéaires (fondamentale) et non linéaires (première harmonique) de l'image observée, et d'un terme de régularisation basé sur la parcimonie afin de stabiliser la solution. Pour tenir compte de l'atténuation en profondeur des images harmoniques, un terme d'atténuation dans le modèle direct de l'image harmonique est proposé sur la base d'une analyse spectrale effectuée sur les signaux RF observés. La méthode proposée a d'abord été appliquée en deux étapes, en estimant d'abord la réponse impulsionelle, suivi par la fonction de réflectivité. Dans un deuxième temps, une solution pour estimer simultanément le réponse impulsionelle et la fonction de réflectivité est proposée, et une autre solution pour prendre en compte la variabilité spatiale du la réponse impulsionelle est présentée. L'intérêt de la méthode proposée est démontré par des résultats synthétiques et in vivo et comparé aux méthodes de restauration conventionnelles

Logarithmic Communication for Distributed Optimization in Multi-Agent Systems

Classically, the design of multi-agent systems is approached using techniques from distributed optimization such as dual descent and consensus algorithms. Such algorithms depend on convergence to global consensus before any individual agent can determine its local action. This leads to challenges with respect to communication overhead and robustness, and improving algorithms with respect to these measures has been a focus of the community for decades. This paper presents a new approach for multi-agent system design based on ideas from the emerging field of local computation algorithms. The framework we develop, LOcal Convex Optimization (LOCO), is the first local computation algorithm for convex optimization problems and can be applied in a wide-variety of settings. We demonstrate the generality of the framework via applications to Network Utility Maximization (NUM) and the distributed training of Support Vector Machines (SVMs), providing numerical results illustrating the improvement compared to classical distributed optimization approaches in each case

Diversité et traitements non-linéaires pour les récepteurs modernes

Depuis le doctorat, les travaux de recherche auxquels j'ai contribué ont porté essentiellement sur des problèmes d'estimation d'un signal d'intérêt noyé dans du bruit. Les domaines d'application visés sont majoritairement le radar, mais aussi le GNSS et l'imagerie ultrasonore. Bien que différents, ces domaines sont soumis à des tendances similaires qui caractérisent ou caractériseront certainement les récepteurs modernes. En effet, les enjeux applicatifs requièrent de repousser sans cesse les limites de performance des traitements : le radariste cherche à détecter des petites cibles dans des environnements de plus en plus difficiles ; en GNSS, des solutions de positionnement haute précision sont recherchées dans des milieux très contraints tels les canyons urbains ; en imagerie médicale, une qualité accrue des images est recherchée pour améliorer les diagnostics, pour ne citer que quelques exemples. Parmi les tendances qui permettront de repousser les performances des récepteurs modernes, deux sont particulièrement présentes dans les travaux conduits jusqu'ici : la diversité des signaux et les traitements non linéaires. Le document illustre ceci en se focalisant sur deux des thématiques de recherche conduites jusqu’ici, à savoir « Le traitement du signal pour des radars de détection à large bande instantanée » et « La poursuite robuste de la phase d'un signal GNSS multifréquence ». Pour conclure, les perspectives de recherche d’un point de vue méthodologique et applicatif sont discutées