Large-Scale Light Field Capture and Reconstruction

This thesis discusses approaches and techniques to convert Sparsely-Sampled Light Fields (SSLFs) into Densely-Sampled Light Fields (DSLFs), which can be used for visualization on 3DTV and Virtual Reality (VR) devices. Exemplarily, a movable 1D large-scale light field acquisition system for capturing SSLFs in real-world environments is evaluated. This system consists of 24 sparsely placed RGB cameras and two Kinect V2 sensors. The real-world SSLF data captured with this setup can be leveraged to reconstruct real-world DSLFs. To this end, three challenging problems require to be solved for this system: (i) how to estimate the rigid transformation from the coordinate system of a Kinect V2 to the coordinate system of an RGB camera; (ii) how to register the two Kinect V2 sensors with a large displacement; (iii) how to reconstruct a DSLF from a SSLF with moderate and large disparity ranges. To overcome these three challenges, we propose: (i) a novel self-calibration method, which takes advantage of the geometric constraints from the scene and the cameras, for estimating the rigid transformations from the camera coordinate frame of one Kinect V2 to the camera coordinate frames of 12-nearest RGB cameras; (ii) a novel coarse-to-fine approach for recovering the rigid transformation from the coordinate system of one Kinect to the coordinate system of the other by means of local color and geometry information; (iii) several novel algorithms that can be categorized into two groups for reconstructing a DSLF from an input SSLF, including novel view synthesis methods, which are inspired by the state-of-the-art video frame interpolation algorithms, and Epipolar-Plane Image (EPI) inpainting methods, which are inspired by the Shearlet Transform (ST)-based DSLF reconstruction approaches

Self-Supervised Light Field Reconstruction Using Shearlet Transform and Cycle Consistency

The image-based rendering approach using Shearlet Transform (ST) is one of the state-of-the-art Densely-Sampled Light Field (DSLF) reconstruction methods. It reconstructs Epipolar-Plane Images (EPIs) in image domain via an iterative regularization algorithm restoring their coefficients in shearlet domain. Consequently, the ST method tends to be slow because of the time spent on domain transformations for dozens of iterations. To overcome this limitation, this letter proposes a novel self-supervised DSLF reconstruction method, CycleST, which applies ST and cycle consistency to DSLF reconstruction. Specifically, CycleST is composed of an encoder-decoder network and a residual learning strategy that restore the shearlet coefficients of densely-sampled EPIs using EPI reconstruction and cycle consistency losses. Besides, CycleST is a self-supervised approach that can be trained solely on Sparsely-Sampled Light Fields (SSLFs) with small disparity ranges ($\leqslant$ 8 pixels). Experimental results of DSLF reconstruction on SSLFs with large disparity ranges (16 - 32 pixels) from two challenging real-world light field datasets demonstrate the effectiveness and efficiency of the proposed CycleST method. Furthermore, CycleST achieves ~ 9x speedup over ST, at least

Densely-sampled light field reconstruction

In this chapter, we motivate the use of densely-sampled light fields as the representation which can bring the required density of light rays for the correct recreation of 3D visual cues such as focus and continuous parallax and can serve as an intermediary between light field sensing and light field display. We consider the problem of reconstructing such a representation from few camera views and approach it in a sparsification framework. More specifically, we demonstrate that the light field is well structured in the set of so-called epipolar images and can be sparsely represented by a dictionary of directional and multi-scale atoms called shearlets. We present the corresponding regularization method, along with its main algorithm and speed-accelerating modifications. Finally, we illustrate its applicability for the cases of holographic stereograms and light field compression.acceptedVersionPeer reviewe

Sparse and Redundant Representations for Inverse Problems and Recognition

Sparse and redundant representation of data enables the description of signals as linear combinations of a few atoms from a dictionary. In this dissertation, we study applications of sparse and redundant representations in inverse problems and object recognition. Furthermore, we propose two novel imaging modalities based on the recently introduced theory of Compressed Sensing (CS). This dissertation consists of four major parts. In the first part of the dissertation, we study a new type of deconvolution algorithm that is based on estimating the image from a shearlet decomposition. Shearlets provide a multi-directional and multi-scale decomposition that has been mathematically shown to represent distributed discontinuities such as edges better than traditional wavelets. We develop a deconvolution algorithm that allows for the approximation inversion operator to be controlled on a multi-scale and multi-directional basis. Furthermore, we develop a method for the automatic determination of the threshold values for the noise shrinkage for each scale and direction without explicit knowledge of the noise variance using a generalized cross validation method. In the second part of the dissertation, we study a reconstruction method that recovers highly undersampled images assumed to have a sparse representation in a gradient domain by using partial measurement samples that are collected in the Fourier domain. Our method makes use of a robust generalized Poisson solver that greatly aids in achieving a significantly improved performance over similar proposed methods. We will demonstrate by experiments that this new technique is more flexible to work with either random or restricted sampling scenarios better than its competitors. In the third part of the dissertation, we introduce a novel Synthetic Aperture Radar (SAR) imaging modality which can provide a high resolution map of the spatial distribution of targets and terrain using a significantly reduced number of needed transmitted and/or received electromagnetic waveforms. We demonstrate that this new imaging scheme, requires no new hardware components and allows the aperture to be compressed. Also, it presents many new applications and advantages which include strong resistance to countermesasures and interception, imaging much wider swaths and reduced on-board storage requirements. The last part of the dissertation deals with object recognition based on learning dictionaries for simultaneous sparse signal approximations and feature extraction. A dictionary is learned for each object class based on given training examples which minimize the representation error with a sparseness constraint. A novel test image is then projected onto the span of the atoms in each learned dictionary. The residual vectors along with the coefficients are then used for recognition. Applications to illumination robust face recognition and automatic target recognition are presented

Optimal sparsity allows reliable system-aware restoration of fluorescence microscopy images

Incluye: artículo, material suplementario, videos y software.Fluorescence microscopy is one of the most indispensable and informative driving forces for biological research, but the extent of observable biological phenomena is essentially determined by the content and quality of the acquired images. To address the different noise sources that can degrade these images, we introduce an algorithm for multiscale image restoration through optimally sparse representation (MIRO). MIRO is a deterministic framework that models the acquisition process and uses pixelwise noise correction to improve image quality. Our study demonstrates that this approach yields a remarkable restoration of the fluorescence signal for a wide range of microscopy systems, regardless of the detector used (e.g., electron-multiplying charge-coupled device, scientific complementary metal-oxide semiconductor, or photomultiplier tube). MIRO improves current imaging capabilities, enabling fast, low-light optical microscopy, accurate image analysis, and robust machine intelligence when integrated with deep neural networks. This expands the range of biological knowledge that can be obtained from fluorescence microscopy.We acknowledge the support of the National Institutes of Health grants R35GM124846 (to S.J.) and R01AA028527 (to C.X.), the National Science Foundation grants BIO2145235 and EFMA1830941 (to S.J.), and Marvin H. and Nita S. Floyd Research Fund (to S.J.). This research project was supported, in part, by the Emory University Integrated Cellular Imaging Microscopy Core and by PHS Grant UL1TR000454 from the Clinical and Translational Science Award Program, National Institutes of Health, and National Center for Advancing Translational Sciences.S

A new convolutional neural network based on combination of circlets and wavelets for macular OCT classification

Artificial intelligence (AI) algorithms, encompassing machine learning and deep learning, can assist ophthalmologists in early detection of various ocular abnormalities through the analysis of retinal optical coherence tomography (OCT) images. Despite considerable progress in these algorithms, several limitations persist in medical imaging fields, where a lack of data is a common issue. Accordingly, specific image processing techniques, such as time–frequency transforms, can be employed in conjunction with AI algorithms to enhance diagnostic accuracy. This research investigates the influence of non-data-adaptive time–frequency transforms, specifically X-lets, on the classification of OCT B-scans. For this purpose, each B-scan was transformed using every considered X-let individually, and all the sub-bands were utilized as the input for a designed 2D Convolutional Neural Network (CNN) to extract optimal features, which were subsequently fed to the classifiers. Evaluating per-class accuracy shows that the use of the 2D Discrete Wavelet Transform (2D-DWT) yields superior outcomes for normal cases, whereas the circlet transform outperforms other X-lets for abnormal cases characterized by circles in their retinal structure (due to the accumulation of fluid). As a result, we propose a novel transform named CircWave by concatenating all sub-bands from the 2D-DWT and the circlet transform. The objective is to enhance the per-class accuracy of both normal and abnormal cases simultaneously. Our findings show that classification results based on the CircWave transform outperform those derived from original images or any individual transform. Furthermore, Grad-CAM class activation visualization for B-scans reconstructed from CircWave sub-bands highlights a greater emphasis on circular formations in abnormal cases and straight lines in normal cases, in contrast to the focus on irrelevant regions in original B-scans. To assess the generalizability of our method, we applied it to another dataset obtained from a different imaging system. We achieved promising accuracies of 94.5% and 90% for the first and second datasets, respectively, which are comparable with results from previous studies. The proposed CNN based on CircWave sub-bands (i.e. CircWaveNet) not only produces superior outcomes but also offers more interpretable results with a heightened focus on features crucial for ophthalmologists

Applied microlocal analysis of deep neural networks for inverse problems

Deep neural networks have recently shown state-of-the-art performance in different imaging tasks. As an example, EfficientNet is today the best image classifier on the ImageNet challenge. They are also very powerful for image reconstruction, for example, deep learning currently yields the best methods for CT reconstruction. Most imaging problems, such as CT reconstruction, are ill-posed inverse problems, which hence require regularization techniques typically based on a-priori information. Also, due to the human visual system, singularities such as edge-like features are the governing structures of images. This leads to the question of how to incorporate such information into a solver of an inverse problem in imaging and how deep neural networks operate on singularities. The main research theme of this thesis is to introduce theoretically founded approaches to use deep neural networks in combination with model-based methods to solve inverse problems from imaging science. We do this by heavily exploring the singularity structure of images as a-priori information. We then develop a comprehensive analysis of how neural networks act on singularities using predominantly methods from the microlocal analysis. For analyzing the interaction of deep neural networks with singularities, we introduce a novel technique to compute the propagation of wavefront sets through convolutional residual neural networks (conv-ResNet). This is achieved in a two-fold manner: We first study the continuous case where the neural network is defined in an infinite-dimensional continuous space. This problem is tackled by using the structure of these networks as a sequential application of continuous convolutional operators and ReLU non-linearities and applying microlocal analysis techniques to track the propagation of the wavefront set through the layers. This then leads to the so-called \emph{microcanonical relation} that describes the propagation of the wavefront set under the action of such a neural network. Secondly, for studying real-world discrete problems, we digitize the necessary microlocal analysis methods via the digital shearlet transform. The key idea is the fact that the shearlet transform optimally represents Fourier integral operators hence such a discretization decays rapidly, allowing a finite approximation. Fourier integral operators play an important role in microlocal analysis, since it is well known that they preserve singularities on functions, and, in addition, they have a closed form microcanonical relation. Also, based on the newly developed theoretical analysis, we introduce a method that uses digital shearlet coefficients to compute the digital wavefront set of images by a convolutional neural network. Our approach is then used for a similar analysis of the microlocal behavior of the learned-primal dual architecture, which is formed by a sequence of conv-ResNet blocks. This architecture has shown state-of-the-art performance in inverse problem regularization, in particular, computed tomography reconstruction related to the Radon transform. Since the Radon operator is a Fourier integral operator, our microlocal techniques can be applied. Therefore, we can study with high precision the singularities propagation of this architecture. Aiming to empirically analyze our theoretical approach, we focus on the reconstruction of X-ray tomographic data. We approach this problem by using a task-adapted reconstruction framework, in which we combine the task of reconstruction with the task of computing the wavefront set of the original image as a-priori information. Our numerical results show superior performance with respect to current state-of-the-art tomographic reconstruction methods; hence we anticipate our work to also be a significant contribution to the biomedical imaging community.Tiefe neuronale Netze haben in letzter Zeit bei verschiedenen Bildverarbeitungsaufgaben Spitzenleistungen gezeigt. Zum Beispiel ist AlexNet heute der beste Bildklassifikator bei der ImageNet-Challenge. Sie sind auch sehr leistungsfaehig fue die Bildrekonstruktion, zum Beispiel liefert Deep Learning derzeit die besten Methoden fuer die CT-Rekonstruktion. Die meisten Bildgebungsprobleme wie die CT-Rekonstruktion sind schlecht gestellte inverse Probleme, die daher Regularisierungstechniken erfordern, die typischerweise auf vorherigen Informationen basieren. Auch aufgrund des menschlichen visuellen Systems sind Singularitaeten wie kantenartige Merkmale die bestimmenden Strukturen von Bildern. Dies fuehrt zu der Frage, wie man solche Informationen in einen Loeser eines inversen Problems in der Bildverarbeitung einbeziehen kann und wie tiefe neuronale Netze mit Singularitaeten arbeiten. Das Hauptforschungsthema dieser Arbeit ist die Einfuehrung theoretisch fundierter konzeptioneller Ansaetze zur Verwendung von tiefen neuronalen Netzen in Kombination mit modellbasierten Methoden zur Loesung inverser Probleme aus der Bildwissenschaft. Wir tun dies, indem wir die Singularitaetsstruktur von Bildern als Vorinformation intensiv erforschen. Dazu entwickeln wir eine umfassende Analyse, wie neuronale Netze auf Singularitaeten wirken, indem wir vorwiegend Methoden aus der mikrolokalen Analyse verwenden. Um die Interaktion von tiefen neuronalen Netzen mit Singularitaeten zu analysieren, fuehren wir eine neuartige Technik ein, um die Ausbreitung von Wellenfrontsaetzen mit Hilfe von Convolutional Residual neuronalen Netzen (Conv-ResNet) zu berechnen. Dies wird auf zweierlei Weise erreicht: Zunaechst untersuchen wir den kontinuierlichen Fall, bei dem das neuronale Netz in einem unendlich dimensionalen kontinuierlichen Raum definiert ist. Dieses Problem wird angegangen, indem wir die besondere Struktur dieser Netze als sequentielle Anwendung von kontinuierlichen Faltungsoperatoren und ReLU-Nichtlinearitaeten nutzen und mikrolokale Analyseverfahren anwenden, um die Ausbreitung einer Wellenfrontmenge durch die Schichten zu verfolgen. Dies fuehrt dann zu einer mikrokanonischen Beziehung, die die Ausbreitung der Wellenfrontmenge unter ihrer Wirkung beschreibt. Zweitens digitalisieren wir die notwendigen mikrolokalen Analysemethoden ueber die digitale Shearlet-Transformation, wobei die Digitalisierung fuer die Untersuchung realer Probleme notwendig ist. Die Schluesselidee ist die Tatsache, dass die Shearlet-Transformation Fourier-Integraloperatoren optimal repraesentiert, so dass eine solche Diskretisierung schnell abklingt und eine endliche Approximation ermoeglicht. Nebenbei stellen wir auch eine Methode vor, die digitale Shearlet-Koeffizienten verwendet, um den digitalen Wellenfrontsatz von Bildern durch ein Faltungsneuronales Netzwerk zu berechnen. Unser Ansatz wird dann fuer eine aehnliche Analyse fuer die gelernte primale-duale Architektur verwendet, die durch eine Sequenz von conv-ResNet-Bloecken gebildet wird. Diese Architektur hat bei der Rekonstruktion inverser Probleme, insbesondere bei der Rekonstruktion der Computertomographie im Zusammenhang mit der Radon-Transformation, Spitzenleistungen gezeigt. Da der Radon-Operator ein Fourier-Integraloperator ist, koennen unsere mikrolokalen Techniken angewendet werden. Um unseren theoretischen Ansatz numerisch zu analysieren, konzentrieren wir uns auf die Rekonstruktion von Roentgentomographiedaten. Wir naehern uns diesem Problem mit Hilfe eines aufgabenangepassten Rekonstruktionsrahmens, in dem wir die Aufgabe der Rekonstruktion mit der Aufgabe der Berechnung der Wellenfrontmenge des Originalbildes als Vorinformation kombinieren. Unsere numerischen Ergebnisse zeigen eine ueberragende Leistung, daher erwarten wir, dass dies auch ein interessanter Beitrag fuer die biomedizinische Bildgebung sein wird

Valokentistä aaltokentiksi: hologrammien generointi perspektiivisistä kuvista

In this thesis, the link between the ray-optics and wave-optics formalisms of light propagation modeling is studied through light field (LF) and holography. Multi-perspective images, such as captured by multicamera arrays, are utilized to obtain the discrete LF information. Three di erent computer generated hologram (CGH) representations are discussed in the thesis: holographic stereogram (an example for incoherent CGH), phase-added stereogram and diffraction specific coherent panoramagram (examples for coherent CGH). Comparative analysis of these three different holographic representation techniques is carried out through experiments simulating the viewing process of the holograms by the human eye. In particular, reconstructed image quality is compared for different scenes at different viewpoints. The accommodation responses of each technique is also evaluated via changing the focal length of the lens in the human eye model to focus the eye at different distances. The prominent issue of speckle noise apparent in hologram reconstruction process is particularly addressed in detail, since it heavily affects the quality of the reconstructed images. In addition to existing solutions analyzed in the thesis, random averaging and pixel separation, a speckle suppression method based on pixel separation for coherent holograms is proposed. The proposed method is shown to further enhance the reconstructed image quality with respect to existing speckle reduction techniques. Besides the perceived image quality, another topic that is seen to be critical in the context of the thesis is simplifying the capture process of LF. In this aspect, the strict camera sampling requirements in LF capture for holographic stereograms are shown to be relieved considerably through the use of shearlet-based LF reconstruction algorithm. This enables utilization of more appropriate capture devices, e.g. multi-camera arrays, instead of conventionally used camera rigs.Tämän työn tavoitteena on tarkastella valon säde- ja aalto-optiikkaa valokenttien ja holografian kautta. Moniperspektiivisiä kuvia käytetään tallentamaan diskreetin valokentän informaatio. Kolme eri digitaalista hologrammiesitystä valittiin tähän työhön vertailtavaksi: holographic stereogram (esimerkkinä inkoherenteista hologrammeista), phase-added stereogram ja diffraction specific coherent panoramagram (esimerkkeinä koherenteista hologrammeista). Näiden hologrammiesitysten välisiä eroja analysoidaan ihmisnäköä numeerisesti simuloivien kokeiden avulla. Erityisesti eri hologrammitallenteista saatujen rekonstruktiokuvien visuaalista laatua vertaillaan simuloimalla katsojaa eri näkökulmista. Holografiseen rekonstruktioprosessiin liittyvää pilkkuhäiriötä käsitellään yksityiskohtaisesti, sillä se heikentää havaittujen kuvien laatua huomattavasti. Nykyisten ratkaisujen, kuten satunnaiskeskiarvottamisen ja pikseliseparaation lisäksi johdetaan pikseliseparaatioon pohjautuva pilkkuhäiriötä vähentävä menetelmä koherenteille hologrammeille. Kokeiden perusteella tämän menetelmän osoitetaan parantavan rekonstruktiokuvien laatua. Havaitun kuvanlaadun lisäksi kriittinen aihe tämän työn kontekstissa on valokentän tallentamisen helpottaminen. Tiukkoja näytteistämisvaatimuksia tähän liittyen voidaan keventää huomattavasti shearlet-muunnokseen pohjautuvan valokentän rekonstruktioalgoritmin avulla, mahdollistaen perinteisesti käytettyjen järjestelmien sijaan käytännöllisempien kameraryhmien käytön