Optical measurement methods are becoming increasingly important for high-precision production of components and quality assurance. The increasing demand can be met by modern imaging systems with advanced optics, such as light field cameras. This work explores their use in the deflectometric measurement of specular surfaces. It presents improvements in phase unwrapping and calibration techniques, enabling high surface reconstruction accuracies using only a single monocular light field camera.

This book provides a comprehensive guide to 3D Light-Field camera based imaging, exploring the working principles, developments and its applications in fluid mechanics and aerodynamics measurements. It begins by discussing the fundamentals of Light-Field imaging and theoretical resolution analysis, before touching upon the detailed optics design and micro-lens array assembly. Subsequently, Light-Field calibration methods that compensate for optical distortions and establish the relations between the image and real-word 3D coordinates are covered. This is followed by Light-Field 3D reconstruction algorithms which are elaborated for micrometer-scale particles and centimeter-scale physical models. Last but not least, implementations of the preceding procedures to selected fundamental and applied flow measurement scenarios are provided at the end of the book. Development and Application of Light-Field Cameras in Fluid Measurements gives an in-depth analysis of each topic discussed, making it ideal as both an introductory and reference guide for researchers and postgraduates interested in 3D flow measurements.

Light field imaging is often presented as a revolution of standard imaging. Indeed, it does bring more control to the user over the final image as the spatio-angular dimensions of the light field offer the possibility to change the viewpoint and refocus after the shot and compute the scene depth map.However, it complicates the work of the optical designer of the system for two reasons. The first is that there exist a multitude of different light field acquisition devices, each with its own specific design. The second is that there is no model that relates the camera design to its optical properties of acquisition and that would guide the designer in his task. This thesis addresses these observations by proposing a first-order optical model to represent any light field acquisition device. This model abstracts a light field camera as en equivalent array of virtual cameras that exists in object space and that performs the same sampling of the scene. The model is used to study and compare several light field cameras as well as a light field microscope setup which reveals guidelines for the conception of light field optical systems. The simulations of the model are also validated through experimentation with a light field camera and a light field microscope that was constructed in our laboratory.

In dieser Arbeit werden spektral kodierte multispektrale Lichtfelder untersucht, wie sie von einer Lichtfeldkamera mit einem spektral kodierten Mikrolinsenarray aufgenommen werden. Für die Rekonstruktion der kodierten Lichtfelder werden zwei Methoden entwickelt, eine basierend auf den Prinzipien des Compressed Sensing sowie eine Deep Learning Methode. Anhand neuartiger synthetischer und realer Datensätze werden die vorgeschlagenen Rekonstruktionsansätze im Detail evaluiert. -In this work, spatio-spectrally coded multispectral light fields, as taken by a light field camera with a spectrally coded microlens array, are investigated. For the reconstruction of the coded light fields, two methods, one based on the principles of compressed sensing and one deep learning approach, are developed. Using novel synthetic as well as a real-world datasets, the proposed reconstruction approaches are evaluated in detail.

The increasing use of automated laser welding processes causes high demands on process monitoring. This work demonstrates methods that use a camera mounted on the focussing optics to perform pre-, in-, and post-process monitoring of welding processes. The implementation uses machine learning methods. All algorithms consider the integration into industrial processes. These challenges include a small database, limited industrial manufacturing inference hardware, and user acceptance.

Deep learning is widely applied to sparse 3D data to perform challenging tasks, e.g., 3D object detection and semantic segmentation. However, the high performance of deep learning comes with high costs, including computational costs and the effort to capture and label data. This work investigates and improves the efficiency of deep learning for sparse 3D data to overcome the obstacles to the further development of this technology.