<p>'Image volumes' refer to realizations of images in other dimensions such as time, spectrum, and focus. Recent advances in scientific, medical, and consumer applications demand improvements in image volume capture. Though image volume acquisition continues to advance, it maintains the same sampling mechanisms that have been used for decades; every voxel must be scanned and is presumed independent of its neighbors. Under these conditions, improving performance comes at the cost of increased system complexity, data rates, and power consumption. </p><p>This dissertation explores systems and methods capable of efficiently improving sensitivity and performance for image volume cameras, and specifically proposes several sampling strategies that utilize temporal coding to improve imaging system performance and enhance our awareness for a variety of dynamic applications. </p><p>Video cameras and camcorders sample the video volume (x,y,t) at fixed intervals to gain understanding of the volume's temporal evolution. Conventionally, one must reduce the spatial resolution to increase the framerate of such cameras. Using temporal coding via physical translation of an optical element known as a coded aperture, the compressive temporal imaging (CACTI) camera emonstrates a method which which to embed the temporal dimension of the video volume into spatial (x,y) measurements, thereby greatly improving temporal resolution with minimal loss of spatial resolution. This technique, which is among a family of compressive sampling strategies developed at Duke University, temporally codes the exposure readout functions at the pixel level.</p><p>Since video cameras nominally integrate the remaining image volume dimensions (e.g. spectrum and focus) at capture time, spectral (x,y,t,\lambda) and focal (x,y,t,z) image volumes are traditionally captured via sequential changes to the spectral and focal state of the system, respectively. The CACTI camera's ability to embed video volumes into images leads to exploration of other information within that video; namely, focal and spectral information. The next part of the thesis demonstrates derivative works of CACTI: compressive extended depth of field and compressive spectral-temporal imaging. These works successfully show the technique's extension of temporal coding to improve sensing performance in these other dimensions.</p><p>Geometrical optics-related tradeoffs, such as the classic challenges of wide-field-of-view and high resolution photography, have motivated the development of mulitscale camera arrays. The advent of such designs less than a decade ago heralds a new era of research- and engineering-related challenges. One significant challenge is that of managing the focal volume (x,y,z) over wide fields of view and resolutions. The fourth chapter shows advances on focus and image quality assessment for a class of multiscale gigapixel cameras developed at Duke.</p><p>Along the same line of work, we have explored methods for dynamic and adaptive addressing of focus via point spread function engineering. We demonstrate another form of temporal coding in the form of physical translation of the image plane from its nominal focal position. We demonstrate this technique's capability to generate arbitrary point spread functions.</p> / Dissertation

We have previously introduced a high throughput multiplexing computational spectral imaging device. The device measures scalar projections of pseudo-arbitrary spectral filters at each spatial pixel. This paper discusses simulation and initial experimental progress in performing computational spectral unmixing by taking advantage of the natural sparsity commonly found in the fractional abundances. The simulation results show a lower unmixing error compared to traditional spectral imaging devices. Initial experimental results demonstrate the ability to directly perform spectral unmixing with less error than multiplexing alone.

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

<p>Metamaterials extend the design space, flexibility, and control of optical material systems and so yield fundamentally new computational imaging systems. A computational imaging system relies heavily on the design of measurement modes. Metamaterials provide a great deal of control over the generation of the measurement modes of an aperture. On the other side of the coin, computational imaging uses the data that that can be measured by an imaging system, which may limited, in an optimal way thereby producing the best possible image within the physical constraints of a system. The synergy of these two technologies - metamaterials and computational imaging - allows for entirely novel imaging systems. These contributions are realized in the concept of a frequency-diverse metamaterial imaging system that will be presented in this thesis. This 'metaimager' uses the same electromagnetic flexibility that metamaterials have shown in many other contexts to construct an imaging aperture suitable for single-pixel operation that can measure arbitrary measurement modes, constrained only by the size of the aperture and resonant elements. It has no lenses, no moving parts, a small form-factor, and is low-cost.</p><p>In this thesis we present an overview of work done by the author in the area of metamaterial imaging systems. We first discuss novel transformation-optical lenses enabled by metamaterials which demonstrate the electromagnetic flexibility of metamaterials. We then introduce the theory of computational and compressed imaging using the language of Fourier optics, and derive the forward model needed to apply computational imaging to the metaimager system. We describe the details of the metamaterials used to construct the metaimager and their application to metamaterial antennas. The experimental tools needed to characterize the metaimager, including far-field and near-field antenna characterization, are described. We then describe the design, operation, and characterization of a one-dimensional metaimager capable of collecting two-dimensional images, and then a two-dimensional metaimager capable of collecting two-dimensional images. The imaging results for the one-dimensional metaimager are presented including two-dimensional (azimuth and range) images of point scatters, and video-rate imaging. The imaging results for the two-dimensional metaimager are presented including analysis of the system's resolution, signal-to-noise sensitivity, acquisition rate, human targets, and integration of optical and structured-light sensors. Finally, we discuss explorations into methods of tuning metamaterial radiators which could be employed to significantly increase the capabilities of such a metaimaging system, and describe several systems that have been designed for the integration of tuning into metamaterial imaging systems.</p> / Dissertation

Optical coherence tomography (OCT) is a biomedical imaging technique with many current applications. A limitation of the technique is its shallow depth of imaging. A major factor limiting imaging depth in OCT is multiple-scattering of light. This thesis proposes an integrated computational imgaging approach to improve depth of imaging in OCT. In this approach ultrasound patterns are used to modulate the refractive index of tissue. Simulations of the impact of ultrasound on the refractive index are performed, and the results are shown in this thesis. Simulations of the impact of the modulated refractive index on the propagation of light in tissue are needed. But there is no suitable simulator available. Thus, we implemented a Monte Carlo method to solve integral equations that could be used to perform these simulations. Results for integral equations in 1-D and 2-D are shown.

Optical coherence tomography (OCT) is a biomedical imaging technique with many current applications. A limitation of the technique is its shallow depth of imaging. A major factor limiting imaging depth in OCT is multiple-scattering of light. This thesis proposes an integrated computational imgaging approach to improve depth of imaging in OCT. In this approach ultrasound patterns are used to modulate the refractive index of tissue. Simulations of the impact of ultrasound on the refractive index are performed, and the results are shown in this thesis. Simulations of the impact of the modulated refractive index on the propagation of light in tissue are needed. But there is no suitable simulator available. Thus, we implemented a Monte Carlo method to solve integral equations that could be used to perform these simulations. Results for integral equations in 1-D and 2-D are shown.

It is well known that a translating mask can optically encode low-resolution measurements from which higher resolution images can be computationally reconstructed. We experimentally demonstrate that this principle can be used to achieve substantial increase in image resolution compared to the size of the focal plane array (FPA). Specifically, we describe a scalable architecture with a translating mask (also referred to as a coded aperture) that achieves eightfold resolution improvement (or 64: 1 increase in the number of pixels compared to the number of focal plane detector elements). The imaging architecture is described in terms of general design parameters (such as field of view and angular resolution, dimensions of the mask, and the detector and FPA sizes), and some of the underlying design trades are discussed. Experiments conducted with different mask patterns and reconstruction algorithms illustrate how these parameters affect the resolution of the reconstructed image. Initial experimental results also demonstrate that the architecture can directly support task-specific information sensing for detection and tracking, and that moving objects can be reconstructed separately from the stationary background using motion priors. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)

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.

Stimulated Raman scattering (SRS) imaging is a chemical imaging scheme that can visualize cellular content based on intrinsic chemical bond vibrations. To resolve chemicals with overlapping Raman bands, spectroscopic SRS platforms have been developed. To date, endeavors on high-speed instrumentation have achieved spectral acquisition at the microsecond level, enabling in vivo imaging of cells and tissues. Nevertheless, due to the extremely small Raman cross-sections, the current performance of SRS is bounded by a design space that trades off speed, signal fidelity, and spectral bandwidth. The lack of tailored data mining algorithms further limits the chemical information one can extract from the spectroscopic images. My thesis work focuses on developing computational SRS imaging approaches to break the physical tradeoffs and novel data analytical tools to decipher essential chemical information from stimulated Raman spectroscopic images. Utilizing data redundancy of spectroscopic images, we developed two compressive sensing schemes to improve the imaging speed by one order of magnitude without information loss. To break the sensitivity limit, we proposed an ultrafast spectroscopic SRS system and further integrated it with a deep neural network to synergistically achieve microsecond level imaging in the fingerprint region. To improve the chemical specificity and content levels, we implemented a sparsity-regularized spectral unmixing algorithm, realizing multiplexed imaging of up to six major metabolites in a cell. Finally, enabled by advances in low-exposure imaging and spectral unmixing, longitudinal imaging of biofuel synthesis in live cells with sophisticated chemical information is demonstrated. / 2022-09-24T00:00:00Z

X-ray computed tomography (CT) is a popular imaging technique used for reconstructing volumetric properties for a large range of objects. Compared to traditional optical means, CT is a valuable tool for analyzing objects with interesting internal structure or complex geometries that are not accessible with. In this thesis, a variety of applications in computer vision and graphics of inverse problems using tomographic imaging modalities will be presented: The first application focuses on the CT reconstruction with a specific emphasis on recovering thin 1D and 2D manifolds embedded in 3D volumes. To reconstruct such structures at resolutions below the Nyquist limit of the CT image sensor, we devise a new 3D structure tensor prior, which can be incorporated as a regularizer into more traditional proximal optimization methods for CT reconstruction. The second application is about space-time tomography: Through a combination of a new CT image acquisition strategy, a space-time tomographic image formation model, and an alternating, multi-scale solver, we achieve a general approach that can be used to analyze a wide range of dynamic phenomena. Base on the second application, the third one is aiming to improve the tomographic reconstruction of time-varying geometries undergoing faster, non-periodic deformations, by a warp-and-project strategy. Finally, with a physically plausible divergence-free prior for motion estimation, as well as a novel view synthesis technique, we present applications to dynamic fluid imaging (e.g., 4D soot imaging of a combustion process, a mixing fluid process, a fuel injection process, and view synthesis for visible light tomography), which further demonstrates the flexibility of our optimization framework.