Browsing by Subject "Computational imaging"

This thesis describes three computational optical systems and their underlying coding strategies. These codes are useful in a variety of optical imaging and spectroscopic applications. Two multichannel cameras are described. They both use a lenslet array to generate multiple copies of a scene on the detector. Digital processing combines the measured data into a single image. The visible system uses focal plane coding, and the long wave infrared (LWIR) system uses shift coding. With proper calibration, the multichannel interpolation results recover contrast for targets at frequencies beyond the aliasing limit of the individual subimages. This thesis also describes a LWIR imaging system that simultaneously measures four wavelength channels each with narrow bandwidth. In this system, lenses, aperture masks, and dispersive optics implement a spatially varying spectral code.

This dissertation studies the coding strategies of computational imaging to overcome the limitation of conventional sensing techniques. The information capacity of conventional sensing is limited by the physical properties of optics, such as aperture size, detector pixels, quantum efficiency, and sampling rate. These parameters determine the spatial, depth, spectral, temporal, and polarization sensitivity of each imager. To increase sensitivity in any dimension can significantly compromise the others.

This research implements various coding strategies subject to optical multidimensional imaging and acoustic sensing in order to extend their sensing abilities. The proposed coding strategies combine hardware modification and signal processing to exploiting bandwidth and sensitivity from conventional sensors. We discuss the hardware architecture, compression strategies, sensing process modeling, and reconstruction algorithm of each sensing system.

Optical multidimensional imaging measures three or more dimensional information of the optical signal. Traditional multidimensional imagers acquire extra dimensional information at the cost of degrading temporal or spatial resolution. Compressive multidimensional imaging multiplexes the transverse spatial, spectral, temporal, and polarization information on a two-dimensional (2D) detector. The corresponding spectral, temporal and polarization coding strategies adapt optics, electronic devices, and designed modulation techniques for multiplex measurement. This computational imaging technique provides multispectral, temporal super-resolution, and polarization imaging abilities with minimal loss in spatial resolution and noise level while maintaining or gaining higher temporal resolution. The experimental results prove that the appropriate coding strategies may improve hundreds times more sensing capacity.

Human auditory system has the astonishing ability in localizing, tracking, and filtering the selected sound sources or information from a noisy environment. Using engineering efforts to accomplish the same task usually requires multiple detectors, advanced computational algorithms, or artificial intelligence systems. Compressive acoustic sensing incorporates acoustic metamaterials in compressive sensing theory to emulate the abilities of sound localization and selective attention. This research investigates and optimizes the sensing capacity and the spatial sensitivity of the acoustic sensor. The well-modeled acoustic sensor allows localizing multiple speakers in both stationary and dynamic auditory scene; and distinguishing mixed conversations from independent sources with high audio recognition rate.

The explosion in the popularity and success of deep learning in the past decade has accelerated the development of computationally efficient, GPU-accelerated frameworks, such as TensorFlow and PyTorch, for rapid prototyping of neural networks. In this dissertation, we show that these deep learning tools are also well-suited for computational 3D imaging problems, specifically optical diffraction tomography (ODT), photogrammetry, and our newly proposed optical coherence refraction tomography (OCRT). Underlying these computational 3D imaging techniques is a physical model that demands multiple measurements taken with either angular diversity, wavelength diversity, or both. This requirement can be compactly summarized as wavevector (or k-vector) diversity, where the magnitude and direction of the wavevector correspond to the color and angle of the light, respectively.

To understand the importance of wavevector diversity for 3D imaging, this dissertation starts by advancing a unified k-space theory of optical coherence tomography (OCT), the most comprehensive and inclusive theoretical description of OCT to date that not only describes the transfer functions of all major forms of OCT and other coherent techniques (e.g., confocal microscopy, holography, ODT), but also includes the fundamental concepts of OCT, such as speckle, dispersion, aberration, and the tradeoff between lateral resolution and depth of focus (DOF).

Consistent with this unified theory, we implemented in TensorFlow a reconstruction algorithm for ODT, a technique that relies on illumination angular diversity to achieve 3D refractive index (RI) imaging. We propose a new method for filling the well-known “missing cone” of the ODT transfer function by reparameterizing the 3D sample as the output of an untrained neural network known as a deep image prior (DIP), which we show to outperform traditional regularization strategies.

Next, we introduce OCRT, a computational extension of OCT that incorporates extreme angular diversity over OCT's already high wavelength diversity to enable resolution-enhanced, speckle-reduced reconstructions that overcome the lateral-resolution-DOF tradeoff. OCRT also jointly reconstructs quantitative RI maps of the sample using a ray-based physical model implemented in TensorFlow. We also demonstrate spectroscopic OCRT (SOCRT), an extension of spectroscopic OCT (SOCT) that overcomes its tradeoff between spectral and axial resolution.

Motivated to make OCRT more widely applicable, we propose a new use of conic-section (e.g., parabolic, ellipsoidal) mirrors to allow fast multi-view imaging over very high angular ranges (up to 360°) using galvanometers without requiring sample rotation. We theoretically characterize the achievable fields of view (FOVs) as a function of many imaging system parameters (e.g., NA, wavelength, incidence angle, focal length, and telecentricity). Based on these predictions, we constructed a parabolic-mirror-based imaging system that facilitates multi-view OCT volume capture with millimetric FOVs over up to ±75°, which we combined to perform 3D OCRT reconstructions of zebrafish, fruitfly, and mouse tissue.

Finally, we adapted the OCRT reconstruction algorithm to photogrammetric 3D mesoscopic imaging with tens-of-micron accuracy, using a sequence of smartphone camera images taken at close range under freehand motion. 3D estimation was possible due to the angular diversity afforded by the nontelecentricity of smartphone cameras, using a similar ray-based model as for OCRT. We show that careful modeling of lens distortion and incorporation of a DIP are both pivotal for obtaining high 3D accuracy using devices not designed for close-range imaging.

Optical imaging is an essential tool for studying life sciences. Existing biomedical optical systems range from microscopes in clinics that use wave optics principles to examine pathological samples at high resolution, to photoplethysmography in everyday smartwatches utilizing diffuse optics technologies for monitoring deep tissue physiology. An optical system, such as a photography solution in a studio, typically consists of three parts: illumination, objects of interest, and recording devices. Over the past decades, thanks to rapid advancements in semiconductor manufacturing, numerous new and exciting optical devices have emerged. These include low-cost, small form-factor LEDs and CMOS camera sensors in budget tablet devices, as well as high-density time-of-flight array detectors in recent generations of iPhones, for example. Moore's Law, on the other hand, has driven significant development in powerful yet inexpensive computational tools. As a result, nowadays, analogous to other medical imaging modalities such as X-ray CT and MRI, multiplexed optical measurements that may not resemble the object of interest can be recorded and post-processed to reconstruct useful images for human perception. In this thesis, several new computational optical imaging techniques at different scales will be discussed. These range from vectorial tomographic microscopies for imaging anisotropic cells and tissue, to high-throughput imaging systems capable of recording eukaryotic colonies at mesoscopic scales, and novel single-photon-sensitive sensing methods for non-invasive imaging of macroscopic transient dynamics deep within turbid volumes.

This dissertation explores computational optical imaging methods to circumvent the physical limitations of classical sensing. An ideal imaging system would maximize resolution in time, spectral bandwidth, three-dimensional object space, and polarization. Practically, increasing any one parameter will correspondingly decrease the others.

Spectrometers strive to measure the power spectral density of the object scene. Traditional pushbroom spectral imagers acquire high resolution spectral and spatial resolution at the expense of acquisition time. Multiplexed spectral imagers acquire spectral and spatial information at each instant of time. Using a coded aperture and dispersive element, the coded aperture snapshot spectral imagers (CASSI) here described leverage correlations between voxels in the spatial-spectral data cube to compressively sample the power spectral density with minimal loss in spatial-spectral resolution while maintaining high temporal resolution.

Photography is limited by similar physical constraints. Low f/# systems are required for high spatial resolution to circumvent diffraction limits and allow for more photon transfer to the film plain, but require larger optical volumes and more optical elements. Wide field systems similarly suffer from increasing complexity and optical volume. Incorporating a multi-scale optical system, the f/#, resolving power, optical volume and wide field of view become much less coupled. This system uses a single objective lens that images onto a curved spherical focal plane which is relayed by small micro-optics to discrete focal planes. Using this design methodology allows for gigapixel designs at low f/# that are only a few pounds and smaller than a one-foot hemisphere.

Computational imaging systems add the necessary step of forward modeling and calibration. Since the mapping from object space to image space is no longer directly readable, post-processing is required to display the required data. The CASSI system uses an undersampled measurement matrix that requires inversion while the multi-scale camera requires image stitching and compositing methods for billions of pixels in the image. Calibration methods and a testbed are demonstrated that were developed specifically for these computational imaging systems.

The emerging theory of compressed sensing has been nothing short of a revolution in signal processing, challenging some of the longest-held ideas in signal processing and leading to the development of exciting new ways to capture and reconstruct signals and images. Although the theoretical promises of compressed sensing are manifold, its implementation in many practical applications has lagged behind the associated theoretical development. Our goal is to elevate compressed sensing from an interesting theoretical discussion to a feasible alternative to conventional imaging, a significant challenge and an exciting topic for research in signal processing. When applied to imaging, compressed sensing can be thought of as a particular case of computational imaging, which unites the design of both the sensing and reconstruction of images under one design paradigm. Computational imaging tightly fuses modeling of scene content, imaging hardware design, and the subsequent reconstruction algorithms used to recover the images.

This thesis makes important contributions to each of these three areas through two primary research directions. The first direction primarily attacks the challenges associated with designing practical imaging systems that implement incoherent measurements. Our proposed snapshot imaging architecture using compressive coded aperture imaging devices can be practically implemented, and comes equipped with theoretical recovery guarantees. It is also straightforward to extend these ideas to a video setting where careful modeling of the scene can allow for joint spatio-temporal compressive sensing. The second direction develops a host of new computational tools for photon-limited inverse problems. These situations arise with increasing frequency in modern imaging applications as we seek to drive down image acquisition times, limit excitation powers, or deliver less radiation to a patient. By an accurate statistical characterization of the measurement process in optical systems, including the inherent Poisson noise associated with photon detection, our class of algorithms is able to deliver high-fidelity images with a fraction of the required scan time, as well as enable novel methods for tissue quantification from intraoperative microendoscopy data. In short, the contributions of this dissertation are diverse, further the state-of-the-art in computational imaging, elevate compressed sensing from an interesting theory to a practical imaging methodology, and allow for effective image recovery in light-starved applications.

Microwave imaging platforms conventionally take the form of antenna arrays or synthetic apertures. Inspired by methods in the optical regime, computational microwave imaging has recently taken hold as an alternative approach that uses spatially-diverse waveforms to multiplex scene information. In this dissertation, we use dynamic metasurface apertures to demonstrate improved hardware characteristics and capabilities in computational microwave imaging systems. In particular, we demonstrate waveguide-fed and cavity-backed dynamic metasurface apertures. A waveguide-fed dynamic metasurface aperture consists of a waveguide device loaded with numerous independently tunable metamaterial elements, each of which couples energy from the guided mode into a reconfigurable radiation pattern. We explicate design considerations for a waveguide-fed dynamic metasurface aperture, optimize its usage, and utilize it in computational imaging. In addition, we leverage the dynamic aperture's agility to demonstrate through-wall imaging and beamforming for synthetic aperture radar. Significant attention is also devoted to imaging with a single frequency, an approach which can ease the complexity and improve the performance of the required RF components.

Expanding on the waveguide-fed instantiation, we investigate cavity-backed dynamic apertures. These apertures employ disordered cavity modes to feed a multitude of radiating elements. We investigate this approach with two structures: a volumetric cavity, where we tune the boundary condition, and a planar PCB-based cavity, where the radiating elements are tuned. Capable of generating diverse radiation patterns, we use these structures to assess the utility of dynamic tuning in computational imaging systems. Many of the architectures studied in this dissertation chart a path toward a low-cost dynamic aperture with a favorable form factor, a platform which provides immense control over its emitted fields for a variety of microwave applications.

Traditional radar imaging systems are implemented using the focal plane

technique, steering beam antennas, or synthetic aperture imaging. These conventional

methods require either a large number of sensors to form a focal plane array similar to the

idea of an optical camera, or a single transceiver mechanically scanning the field of view.

The former results in expensive systems whereas the latter results in long acquisition time.

Computational imaging methods are widely used for the ability to acquire information

beyond the recorded pixels, thus are ideal options for reducing the number of radar

sensors in radar imaging systems. Novel antenna designs such as the frequency diverse

antennas are capable of optimizing antennas for computational imaging algorithms. This

thesis tries to find a solution for improving the efficiency of radar imaging using a method

that combines computational imaging and novel antenna designs. This thesis first

proposes two solutions to improve the two aspects of the tradeoff respectively, i.e. the

number of sensors and mechanical scanning. A method using time-of-flight imaging

algorithm with a sparse array of antennas is proposed as a solution to reduce the number

of sensors required to estimate a reflective surface. An adaptive algorithm based on the

Bayesian compressive sensing framework is proposed as a solution to minimize

mechanical scanning for synthetic aperture imaging systems. The thesis then explores the

feasibility to further improve radar imaging systems by combining computational

imaging and antenna design methods as a solution. A rapid prototyping method for

manufacturing custom-designed antennas is developed for implementing antenna

designs quickly in a laboratory environment. This method has facilitated the design of a

frequency diverse antenna based on a leaky waveguide design, which can be used under

computational imaging framework to perform 3D imaging. The proposed system is

capable of performing imaging and target localization using only one antenna and

without mechanical scanning, thus is a promising solution to ultimately improve the

efficiency for radar imaging.

The advent of resonant metamaterials with strongly dispersive behavior allowed scientists to design new electromagnetic devices -- including (but not limited to) absorbers, antennas, lenses, holograms, and arguably the most well-known of them all, invisibility cloaks -- exhibiting properties that would otherwise be difficult to obtain. At the heart of these breakthrough designs is our ability to model the behavior of individual metamaterial elements as Lorentzian dipoles, and -- in applications that call for it -- collectively model an entire array of such elements as a homogenous medium with effective electromagnetic properties retrieved from measurements or simulations.

Of particular interest in the context of this dissertation is a certain type of metamaterials elements which -- while composed entirely of essentially non-magnetic materials -- respond to a magnetic field, can be modeled as magnetic dipoles, and are able to form a material with effective magnetic response. This thesis describes how such ``magnetic metamaterials'' have been utilized by the author when designing devices for applications in wireless power transfer (WPT) and computational imaging. For the former, I discuss in the thesis a metamaterial implementation of a magnetic `superlens' for wireless power transfer enhancements, and a magnetic reflector for near field shielding. For the latter I detail how we model the imaging capabilities of a recently-introduced class of dispersive metamaterial-based leaky apertures that produce pseudo-random measurement modes, and demonstration of novel Lorentzian-constrained holograms able to tailor their radiation patterns.

To design a magnetic superlens for WPT enhancements, we first demonstrate how an array comprising resonant metamaterial elements can act as an effective medium with negative permeability ($\mu$) and enhance near-field transmission of quasi-static non-resonant coil antennas. We implement a new technique to retrieve all diagonal components of our superlens' permeability, including its normal component, which standard techniques cannot retrieve. We study the effect of different components of the $\mu$ tensor on field enhancements using analytical solutions as well as 2D rotationally-symmetric full-wave simulations which approximate the lens as a disc of equal diameter, enabling highly efficient axisymmetric description of the problem. Our studies indicate enhancements are strongest when all three diagonal components of Re$(\mu)$ are negative, which we attribute to the excitation of surface waves.

The ability to retrieve permeability's normal component, awarded to us with the implementation of the aforementioned retrieval technique, directly enabled the design of a near field magnetic shield, which -- in contrast to the tripple-negative superlens -- relies on the normal component of $\mu$ assuming values near zero. The thesis discusses the theory behind this phenomenon and explains why such an anisotropic slab is capable of reflecting magnetic fields with component of their wave vector parallel to the slab's surface (fields which contain significant portions of the energy transferred in WPT systems with dipole-like coils). Furthermore, the dispersive nature of the resonant metamaterials used to realize the shield grants us the ability to block certain frequencies while allowing the transmission of other, which can be particularly useful in certain applications; conventional materials used for shielding or electromagnetic interference (EMI) suppression, on the other hand, block frequencies indiscriminately.

The thesis also discusses a single-pixel, metamaterial-based aperture we designed for computational imaging purposes. This aperture, termed \textit{metaimager}, forms pseudo-random radiation patterns that vary with frequency by leaking energy from a guided mode via a collection of randomly distributed resonant metamaterial elements. The metaimager, then, is able to interrogate a scene without any moving parts or expensive auxiliary hardware (both are common problems which plague synthetic aperture and phased array systems, respectively). While such a structure cannot be homogenized, when modeling its imaging capabilities we still rely on the fact each of its irises can be modeled analytically as a magnetic dipole using a relatively simple Lorentzian expression. Accurate qualitative modeling of such apertures is of paramount importance in the design and optimization stages, since it allows us to save time and money by avoiding prohibitively slow full-wave simulations of such complex structures and unnecessary fabrication processes.

Lastly, the thesis discusses how such an aperture can be viewed as a hologram in which pixels are realized by the metamaterial elements and the reference wave is realized by the fields that excite them. While the current metaimager implementation produces pseudo-random modes, the last section of the thesis discusses how, by accounting for the Lorentzian constraints of each pixel, a novel metamaterial hologram can be designed to yield tailored radiation patterns. An experiment utilizing a Fraunhofer hologram excited in a free-space illumination configuration indicates tailored modes can indeed be formed by carefully choosing the resonance frequency and location of each metamaterial. While this proof-of-concept example is relatively simple, more sophisticated realizations of such holograms can be explored in future works.

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.

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.

Metasurface apertures provide an alternative approach to the very commonly used phased arrays or electronic scanned antennas (ESA) for wireless power transfer (WPT) and imaging applications. Array antennas use radiating elements which are often spaced at half-wavelength and uses active phase-shifter at each module to control the phase. However, in a metasurface antenna, the required phase is obtained from the sampled reference wave which propagates over the aperture providing an advance phase to each radiating elements. Metasurface apertures have very low manufacturing costs, planar form factor making them a suitable candidate for applications involving beamforming and wavefront shaping.

The thesis is divided mainly into two parts consisting of designing metasurface apertures for WPT applications and computational imaging purposes. In the first part, the proposed WPT system operating by focusing fields in the Fresnel region is presented with two proof of concept demonstrations. The first demonstration includes patch array antennas as the transmit and receive aperture and a half-wave rectifier to convert the RF to DC. The frequency of operation is 5.8 GHz (C-band) and the design of the patch array antenna is tedious and not suitable for a dynamic aperture which is possible by making use of metasurface apertures. The second demonstration consists of a metasurface aperture which uses a holographic technique to achieve focusing of microwaves at a particular focus distance for the transmit aperture. The receiving aperture is a metamaterial absorber which is connected to a rectifying circuit to harvest the power, thereby completing the WPT system. A LED connected as the load is illuminated which indicates the basic functionality of the WPT system. The RF-DC power transfer efficiency is in good agreement between simulations and experiments. The proposed system consisting of metasurfaces for both the transmit (focused aperture) and receive aperture (absorber) operates at 20 GHz (in K-band) has not been demonstrated in the literature and is a suitable candidate for higher frequencies (W-Band).

The second part consists of designing metasurface apertures demonstrating monostatic and bi-static microwave imaging systems where the metasurface apertures are frequency-diverse and operating at K-band frequencies (18-26.5 GHz). The metasurface apertures consist of radiating irises distributed over the sub-apertures in a periodic pattern. This frequency-diverse aperture produces distinct radiation patterns as a function of frequency that encode scene information onto a set of measurements; images are subsequently reconstructed using computational imaging approaches. In the monostatic case, the metasurface aperture is used as the transit aperture and 4 open-ended waveguides are used as receive aperture. In the case of bistatic case, both the transmit and receive apertures are metasurface apertures which result in increased mode diversity resulting in improved image reconstructions.

Artificial neural networks fundamentally shift the paradigm of computational imaging. Powerful neural processing is not only taking place of the conventional algorithms, but also embracing radical and physically plausible forward models that better sample the high dimensional light field. Physical designs of sampling in turn tailor simulation and neural algorithms for optimal inverse estimation. Sampling, simulation and neural algorithms as three essential components compose a novel imaging paradigm -- artificial neural imaging, in which they interact and improve themselves in an upward spiral.

Here we present three concrete examples of artificial neural imaging and the important roles physical designs play. In all-in-focus imaging, we see autofocus, sampling and fusion algorithms are redefined for optimizing the image quality of a camera with limited depth of field. Image-based neural autofocus acts 5-10x faster than traditional algorithms. The focus control based on the rule or reinforcement learning dynamically estimates the environment and optimizes the focus trajectory. Along with the neural fusion algorithm, the pipeline outperforms traditional focal stacking approaches in static and dynamic scenes. In scatter ptychography, we show imaging the secondary scatters reflected by a remote target under coherent illumination can create a synthetic aperture on the scatterer. The reconstruction of the object through phase retrieval algorithms can drastically exceed the resolution of directly viewing the target. In the lab experiment we demonstrate 32x resolution improvement relative to direct imaging using error-reduction and plug-and-play algorithms. In array camera imaging, we demonstrate heterogeneous multiaperture designs that have better sampling structures and physics-aware transformers for feature-based data fusion. The proposed transformer incorporates the physical information of the camera array as its receptive fields, demonstrating the superior ability of image compositing on array cameras with diverse resolutions, focal lengths, focal planes, color spaces, and exposures. We also demonstrate a scalable pipeline of data synthesis through computer graphics software that empowers the transformers.

The examples above justify artificial neural imaging and the physical designs interweaved. We expect better designs in sampling, simulation, neural algorithms and eventually better estimation of the light field.

DNA, the blueprint of life, is more than a carrier of genetic information. It offers a highly programmable substrate that can be used for computing, nanorobotics, and advanced imaging techniques. In this work, we use the programmable nature of synthetic DNA to engineer two novel applications. In the first part, DNA is programmed to improve the multiplexing capabilities of a fluorescence microscope while in the second part, we design a novel DNA computing architecture that uses a strand displacing polymerase enzyme. This thesis is a collection of 2 experimental papers, 2 theory papers, and 1 software paper. The general theme of this thesis is to exploit the programmable nature of DNA to develop new applications for the wider field of molecular biology, nanoimaging, and computer engineering.

Optical multiplexing is defined as the ability to study, detect, or quantify multiple objects of interest simultaneously. There are several ways to improve optical multiplexing, namely, using orthogonal wavelengths, multiple mesoscale geometries, orthogonal nucleic acid probes, or a combination of these. Most traditional techniques employ either the geometry or the color of single molecules to uniquely identify (or barcode) different species of interest. However, these techniques require complex sample preparation and multicolor hardware setup. In this work, we introduce a time-based amplification-free single-molecule barcoding technique using easy-to-design nucleic acid strands. A dye-labeled complementary reporter strand transiently binds to the programmed nucleic acid strands to emit temporal intensity signals. We program the DNA strands to emit uniquely identifiable temporal signals for molecular-scale fingerprinting. Since the reporters bind transiently to DNA devices, our method offers relative immunity to photobleaching. We use a single universal reporter strand for all DNA devices making our design extremely cost-effective. We show DNA strands can be programmed for generating a multitude of uniquely identifiable molecular barcodes. Our technique can be easily incorporated with the existing orthogonal methods that use wavelength or geometry to generate a large pool of distinguishable molecular barcodes thereby enhancing the overall multiplexing capabilities of single-molecule imaging. The proposed project has exciting transformative potential for nanoscale applications in fluorescence microscopy and cell biology since the development of temporal barcodes would allow for applications such as sensing miRNAs which are largely associated with disease diagnosis and therapeutics.

The regulation of cellular and molecular processes typically involves complex biochemical networks. Synthetic nucleic acid reaction networks (both enzyme-based and enzyme-free) can be systematically designed to approximate sophisticated biochemical processes. However, most of the prior experimental protocols for chemical reaction networks (CRNs) relied on either strand-displacement hybridization or restriction and exonuclease enzymatic reactions. These resulting synthetic systems usually suffer from either slow rates or leaky reactions. This work proposes an alternative architecture to implement arbitrary reaction networks, that is based entirely on strand-displacing polymerase reactions with nonoverlapping I/O sequences. First, the design for a simple protocol that can approximate arbitrary unimolecular and bimolecular reactions using polymerase strand displacement reactions is presented. Then these fundamental reaction systems are used as modules to show large-scale applications of the architecture, including an autocatalytic amplifier, a molecular-scale consensus protocol, and a dynamic oscillatory system. Finally, we engineer an \textit{in vitro} catalytic amplifier system as a proof-of-concept of our polymerase architecture since such sustainable amplifiers require careful sequence design and implementation.

'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.

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.

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.

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.

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.

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.