As a new form of volumetric media, a Light Field (LF) can provide users with a true 6 Degrees-Of-Freedom (DOF) immersive experience, because LF captures the scene with photo-realism, including aperture-limited changes in viewpoint. Nevertheless, the larger size and higher dimension of LF data bring greater challenges to processing and transmission. The main focus of this study is the application of the applied Artificial Intelligence (AI) method to the transmission and processing of LF data, thereby alleviating the performance bottleneck in existing methods. Uncompressed LF data are too large for network transmission, which is why LF compression has become an important research topic. A new LF compression algorithm based on Graph Neural Network (GNN) is proposed in this work. It can use the graph network model to fit the similarity between the LF viewpoints, so that only the data of a few essential anchor viewpoints need to be transmitted after compression, and a complete LF matrix can be reconstructed according to the graph model at the decoding end. This method also solves the problem of weak generalization of the LF reconstruction algorithm when dealing with high-frequency components through the design of two-layer compression structure. Compared with existing compression methods, a higher compression ratio and better quality can be achieved using this algorithm. Furthermore, to improve the adaptability of the real-time requirements of different LF applications and robustness requirements in unreliable network environments, an adaptive LF video transmission scheme based on Multiple Description Coding (MDC) is proposed. It can divide the LF matrix into LF descriptions at different levels of downsampling ratios, and optimize the scheduling of the descriptions transmission queue, which can ensure that it can adaptively adjust the design of basic GNN unit so that the proposed method can adapt more flexibly to the real-time changes of user viewpoint requests, so as to save unnecessary viewpoint transmission overhead to the greatest extent, and minimize the adverse impact of network packet loss and network status fluctuations on LF transmission services. For LF processing, depth estimation has been a very hot topic in recent years. To achieve a good balance between the performance of both narrow- or wide-baseline LF data, a novel optical-flow-based LF depth estimation scheme, which uses a convolutional neural network (CNN) to predict the patch matrix after optical flow offset, is proposed. After the optical-flow-assisted offset, the disparity between patches is processed to a unified numerical range, which can effectively solve the overfitting problem of the LF depth estimation network caused by the uneven distribution of the baseline range of LF samples. Experimental results show that the proposed uniform-patch-based estimation mechanism has good generalization on LF data of different baselines and is compatible with various existing narrow-baseline LF depth estimation algorithms. Finally, since LF processing places high requirements on both the computing and caching capabilities of the infrastructure, a framework that combines Multi-access Edge Computing (MEC) technology with LF applications is proposed in this thesis. In this study, the problem is transformed by the Lyapunov optimization, and an optimized search algorithm based on the Markov approximation method is designed, which can adaptively schedule and adjust the task offloading strategy and resource allocation scheme, so as to provide users with the best service experience in the LF viewpoint interpolation task. Numerical results demonstrate that this edge-based framework can achieve a dynamic balance between energy and caching consumption while meeting the low latency requirements of LF applications.

L’évolution des technologies vidéo permet des expériences de plus en plus immersives. Cependant, les technologies 3D actuelles sont encore très limitées et offrent des situations de visualisation qui ne sont ni confortables ni naturelles. La prochaine génération de technologies vidéo immersives apparait donc comme un défi technique majeur, en particulier avec la prometteuse approche light-field (LF). Le light-field représente tous les rayons lumineux dans une scène. De nouveaux dispositifs d’acquisition apparaissent, tels que des ensembles de caméras ou des appareils photo plénoptiques (basés sur des micro-lentilles). Plusieurs sortes de systèmes d’affichage ciblent des applications immersives, comme les visiocasques ou les écrans light-field basés sur la projection, et des applications cibles prometteuses existent déjà (e.g. la vidéo 360°, la réalité virtuelle, etc.). Depuis plusieurs années, le light-field a stimulé l’intérêt de plusieurs entreprises et institutions, par exemple dans des groupes MPEG et JPEG. Les contenus light-feld ont des structures spécifiques et utilisent une quantité massive de données, ce qui représente un défi pour implémenter les futurs services. L'un des buts principaux de notre travail est d'abord de déterminer quelles technologies sont réalistes ou prometteuses. Cette étude est faite sous l'angle de la compression image et vidéo, car l'efficacité de la compression est un facteur clé pour mettre en place ces services light-field sur le marché. On propose ensuite des nouveaux schémas de codage pour augmenter les performances de compression et permettre une transmission efficace des contenus light-field sur les futurs réseaux. / Evolutions in video technologies tend to offer increasingly immersive experiences. However, currently available 3D technologies are still very limited and only provide uncomfortable and unnatural viewing situations to the users. The next generation of immersive video technologies appears therefore as a major technical challenge, particularly with the promising light-field (LF) approach. The light-field represents all the light rays (i.e. in all directions) in a scene. New devices for sampling/capturing the light-field of a scene are emerging fast such as camera arrays or plenoptic cameras based on lenticular arrays. Several kinds of display systems target immersive applications like Head Mounted Display and projection-based light-field display systems, and promising target applications already exist. For several years now this light-field representation has been drawing a lot of interest from many companies and institutions, for example in MPEG and JPEG groups. Light-field contents have specific structures, and use a massive amount of data, that represent a challenge to set up future services. One of the main goals of this work is first to assess which technologies and formats are realistic or promising. The study is done through the scope of image/video compression, as compression efficiency is a key factor for enabling these services on the consumer markets. Secondly, improvements and new coding schemes are proposed to increase compression performance in order to enable efficient light-field content transmission on future networks.

The digital photography is still trying to catch-up with its analogous counterpart and recording light direction is one of the most recent area of interest. The first and still the only one light-field camera for consumers, the Lytro camera, has reached market in 2011. This work introduces the light-field theory and recording with special emphasis on illustrating the principles in 2D, gives an overview of current hardware and ongoing research in the area and analyses the Lytro camera itself, describing the closed file formats and protocols it uses so that further research can be conducted. An important contribution of the work is a .NET portable library for developers, supplemented by a file editor as well as an application for wireless communication with the camera based on the library. Finally, the theory is used to discuss implications for light-field registration and linear panoramas. Powered by TCPDF (www.tcpdf.org)

Light field photography offers a new approach to digitally captured images. These commercially available cameras are able to capture the 4D light field in a single image. This allows for a variety of image processing capabilities that traditional cameras do not offer. For example, the image can be digitally refocused after it is captured and its depth can be estimated. In terms of application, these capabilities could be beneficial on airborne platforms. However, a limitation of currently available light field cameras is that they are not fully functional at medium or long ranges. If these cameras were to capture light fields at longer ranges, they would have a practical application when mounted on low-flying aircrafts. This dissertation takes current light field photography techniques and modifies them so they work better to capture medium-range images. The majority of cameras that capture the 4D light field use a microlens array to modulate the incoming light before it hits the image sensor. Previous work using printed modulation masks garnered the same effect obtained by microlens arrays. This dissertation details the development of a modulation mask that has medium-range applications. A new way of extracting the 4D light field from raw images that uses a digital Fourier transform is presented. This method works for images captured with microlens arrays and printed mask cameras. Two prototype cameras were built and tested to demonstrate some of these concepts. The concepts demonstrated by these cameras could be used in the future designs of light field cameras.

We present a framework for generating, compressing and rendering of SurfaceLight Field (SLF) data. Our method is based on radiance data generated usingphysically based rendering methods. Thus the SLF data is generated directlyinstead of re-sampling digital photographs. Our SLF representation decouplesspatial resolution from geometric complexity. We achieve this by uniform samplingof spatial dimension of the SLF function. For compression, we use ClusteredPrincipal Component Analysis (CPCA). The SLF matrix is first clustered to lowfrequency groups of points across all directions. Then we apply PCA to eachcluster. The clustering ensures that the within-cluster frequency of data is low,allowing for projection using a few principal components. Finally we reconstructthe CPCA encoded data using an efficient rendering algorithm. Our reconstructiontechnique ensures seamless reconstruction of discrete SLF data. We applied ourrendering method for fast, high quality off-line rendering and real-time illuminationof static scenes. The proposed framework is not limited to complexity of materialsor light sources, enabling us to render high quality images describing the full globalillumination in a scene.

RM257 is a photopolymerisable diacrylate mesogen. In its normal way, the polymerization of the diacrylate mesogen is initiated by using UV light. RM257 mesogen have photopolymeriable acrylate endgroups. RM257 molecules form to polymer network. In this study, we use 632.8 nm HeNe laser to initiate the monomer conversion of RM257. The effects of light intensity, exposure time and temperature of monomer on the rate of polymerization have been studied. The monomer conversion of RM257 is found to have a higher rate when the mesogen in the isotropic phase. On other hand, using a two ¡Vbeen interference pattern, a periodic structure was be produced in the RM257 polymer network. The mesogen has a uniform ordered. To discuss that diacylate effected by holographic light field.

Imaging systems often make use of macroscopic lenses to manipulate light. Modern microfabrication techniques, however, have opened up a pathway to the development of novel arrayed imaging systems. In such systems, centimeter-scale areas can contain thousands to millions of micro-scale optical elements, presenting exciting opportunities for new imaging applications. We show two such applications in this thesis: pressure sensing in microfluidics and high throughput fluorescence microscopy for high content screening. Conversely, we show that arrayed elements are not always needed for three dimensional light field imaging. / Engineering and Applied Sciences

This thesis presents a GPU accelerated method to compress light field or light field videos. The implementation is based on an earlier work of a full light field compression framework. The large amount of data storage by capturing light fields is a challenge to compress and we seek to accelerate the encoding part. We compress by projecting each data point onto a set of dictionaries and seek a sparse representation with the least error. An optimized greedy algorithm to suit computations on the GPU is presented. We benefit of the algorithm outline by encoding the data segmentally in parallel for faster computation speed while maintaining the quality. The results shows a significantly faster encoding time compared to the results in the same research field. We conclude that there are further improvements to increase the speed, and thus it is not too far from an interactive compression speed.

Abstract This work presents the development of a new optical imaging system. Previous objections have claimed that it is easier to build a single good quality field lens than a large number of good microlenses and it is therefore better to use a field objective. The possible benefits from a field lens are here traded for a more compact and cost-efficient design that would be suitable for field diagnostics. The new imaging setup described in this work is based on a microlens array capable of capturing light field data and no other refractive optics are used. Hundreds of lenses with a diameter range 100 to 200 µm are used to capture small elementary images containing a small part of the sample. The design uses a single light source aperture enabling signal separation between the elementary images from the neighboring lenses. Prior art uses, for example, physical structures behind lenses for signal separation, making the suggested approach less complex. Further, the possibility for using printed microlens arrays for imaging instead of expensive glass lenses is studied. The captured light field data consisting of elementary images must be rendered for human viewing. A new method is developed where the rendering is based on gathering the resulting pixel values on a plane set freely in object space, enabling single pass rendering with possibilities to apply statistics to the contributing data improving the rendering quality. Commonly used projection or mosaicing based integration approaches do not allow this. The developed system has its resolution limited by the camera sensor pixel size and objects a few micrometers in size can be resolved. The results show that the imaging setup can be used to capture semi-microscopic images without expensive magnifying optics and it is useful in selected applications. For example, it is shown that the eggs of parasites causing Schistosomiasis can be detected automatically in a microscope sample. It is estimated that the system could be mass produced at low cost. The new system has no moving parts making it less susceptible to mechanical failures and it is compact in comparison with conventional microscopes. It could be a part of a point of care solution needed in diagnostic fieldwork. / Tiivistelmä Tässä väitöskirjassa kuvataan ja tarkastellaan uutta mikrolinsseihin perustuvaa mikroskooppista kuvantamismenetelmää. Aiemmin mikrolinssejä on käytetty tavanomaisten mikroskooppien ominaisuuksien laajentamiseen. Tässä työssä perinteiset mikroskooppiobjektit korvataan linssimatolla, kompaktin ja kustannustehokkaan rakenteen saavuttamiseksi. Käyttökohteena laitteelle on kenttädiagnostiikka. Uusi kuvausjärjestelmä perustuu mikrolinssimattoihin, joilla pystytään näytteistämään valokenttää. Muuta taittavaa optiikka ei käytetä. Sadat halkaisijaltaan 100-200 µm olevat linssit kuvaavat kukin pienen osan näytteestä. Linssien välisten signaalien sekoittumisen estämiseen käytetään hyvin kontrolloitua valonlähdettä. Aiemmin esitetyissä ratkaisuissa käytetään esimerkiksi fyysisiä rakenteita yksittäisten linssien takana. Nyt esitetty ratkaisu on yksinkertaisempi. Työssä esitetään uusi menetelmä osakuvista muodostuvan datan rekonstruktioon. Tuloskuvien muodostamiseksi pikselien arvot kerätään rekonstruktiopinnalle, joka on sijoitettu vapaasti esineavaruuteen. Tämä mahdollistaa laskennallisesti tehokkaan tuloskuvan muodostuksen, sekä tilastollisten menetelmien käytön tuloksen laadun parantamiseen. Kehitetyn järjestelmän resoluutiota rajoittaa kameran pikselikoko ja sillä voidaan havaita muutaman mikrometrin kokoisia kohteita. Tulokset osoittavat, että kuvausmenetelmä sopii mikroskooppisten kohteiden kuvaamiseen ilman kalliita suurentavia linssejä. Menetelmän käyttökelpoisuutta havainnollistetaan, muun muassa, automaattisella Schistosoma parasiitin munien tunnistuksella virtsanäytteestä. Uusi kuvausjärjestelmä on mahdollista toteuttaa edullisesti, siinä ei ole liikkuva osia ja se on pieni verrattuna tavanomaiseen mikroskooppiin. Esitetty ratkaisu soveltuu yhdeksi vaihtoehdoksi kenttädiagnostiikan tarpeisiin.

Light field imaging, specifically synthetic aperture (SA) refocusing is a method used to combine images from an array of cameras to generate a single image with a narrow depth of field that can be positioned arbitrarily throughout the volume under investigation. Creating a stack of narrow depth of field images at varying locations generates a focal stack that can be used to find the location of objects in three dimensions. SA refocusing is particularly useful when reconstructing particle fields that are then used to determine the movement of the fluid they are entrained in, and it can also be used for shape reconstruction. This study applies SA refocusing to reacting flows and microscopic flows by performing shape reconstruction and 3D PIV on a flame, and 3D PIV on flow through a micro channel. The reacting flows in particular posed problems with the method. Reconstruction of the flame envelope was successful except for significant elongation in the optical axis caused by cameras viewing the flame from primarily one direction. 3D PIV on reacting flows suffered heavily from the index of refraction generated by the flame. The refocusing algorithm used assumed the particles were viewed through a constant refractive index (RI) and does not compensate for variations in the RI. This variation caused apparent motion in the particles that obscured their true locations making the 3D PIV prone to error. Microscopic PIV (µPIV) was performed on a channel containing a backward facing step. A microlens array was placed in the imaging section of the setup to capture a light field from the scene, which was then refocusing using SA refocusing. PIV on these volumes was compared to a CFD simulation on the same channel. Comparisons showed that error was most significant near the boundaries and the step of the channel. The axial velocity in particular had significant error near the step were the axial velocity was highest. Flow-wise velocity, though, appeared accurate with average flow-wise error approximately 20% throughout the channel volume.