Fluorescence microscopy has become an indispensable technology to push fundamental neuroscience by recovering labeled neural structures with high resolution. To enable these studies, the field has adopted the use of low-cost widefield 1-photon epi-fluorescence microscopes to image fixed samples and miniaturized head-mounted miniscopes to monitor neural activity in freely behaving animals. However, fluorescence imaging platforms face a number of challenges such as a limited depth of field (DoF), lack of optical sectioning, and susceptibility to scattering and aberrations which compromises the image quality and signal fidelity. As a result, neural studies are often constrained to a shallow volume near the surface of the sample and are limited by high noise and background. To overcome these challenges, this thesis introduces two novel frameworks that combine pupil engineering with computational imaging to push the performance of miniaturized and tabletop fluorescence neural imaging platforms. These strategies will directly optimize and integrate custom phase elements on the often-vacant pupil plane to enable the encoding of extended fluorescence signals by designing a point spread function (PSF) that exhibits an extended depth of field (EDoF) in scattering media. Next, these strategies will use tailored post processing algorithms to recover that extended information from the resulting images. As a result, this strategy allows for the recovery of sources in an extended neural volume without compromising the optical resolution or imaging speed on the underlying platform. First, this thesis introduces EDoF-Miniscope, a miniaturized neural imaging platform which utilizes a novel physics-informed genetic algorithm to optimize a lightweight binary diffractive optical element (DOE) on the pupil plane. By integrating the binary DOE into a prototype platform, EDoF-Miniscope is able to achieve a 2.8x extension in the DoF between twin imaging foci in neural samples. To enable the recovery of the extended sources, this thesis utilizes a straightforward post-processing filter, which can recover neuronal signals with an SBR down to 1.08. Overall, this framework introduces a generalizable, compact and lightweight solution for augmenting miniscopes with a computational EDoF. Next, I improve upon the proposed framework by designing a flexible 1-photon widefield tabletop platform, entitled EDoF-Tabletop, that exhibits comparable field-of-view (FoV, FoV = 0.6x0.6mm), numerical aperture (NA, NA = 0.5) and aberrations to a miniscope. This platform utilizes a spatial light modulator (SLM) on the pupil plane to rapidly deploy optimized pupil phase profiles without the need of manufacturing, aligning and integrating miniaturized optics. EDoF-Tabletop incorporates a deep optics pipeline, which utilizes novel physical modeling, initialization and training strategies to simultaneously and reliably learn a user-defined EDoFs and a reconstruction using synthetic-only data. As a result, EDoF-Tabletop is able to encode and recover signals from EDoFs up to 140-microns deep in neural samples and 400-microns deep in non-scattering samples. By combining pupil engineering with computational imaging, EDoF-Miniscope and EDoF-Tabletop showcase the potential to enhance neural imaging platforms by extracting information from extended volumes in the brain. By focusing on flexible optimization algorithms and rapid prototyping capabilities, the advancements introduced in this thesis promise broader utility across fluorescence microscopy, where capturing detailed information from complex biological samples is essential for advancing scientific understanding.

<p dir="ltr">Imaging in low light is difficult because few photons can arrive at the sensor in a particular time interval. Increasing the exposure time is not always an option, as images will be blurry if the scenes are dynamic. If scenes or objects are moving, one can capture multiple frames with short exposure time and fuse them using carefully designed algorithms; however, aligning the pixels in adjacent frames is challenging due to the high photon shot noise and sensor read noise at low light. If the dynamic range of the scene is high, one needs to further blend multiple exposures from the frames. This blending requires removal of spatially varying noise at various lighting conditions while todays high dynamic range (HDR) fusion algorithms usually assume well illuminated scenes. Therefore, this low-light HDR imaging problem remains unsolved. </p><p dir="ltr">To address these dynamic low-light imaging problems, researches in this dissertation explore both conventional CMOS image sensors and a new type of image sensor, named quanta image sensor (QIS), develop models of the imaging conditions of interest, and propose new image reconstruction algorithms based on deep neural networks together with new training protocols to assist the learning. Researches in this dissertation target to reconstruct dynamic HDR scenes at a light level of 1 photon per pixel (ppp) or less than 1 lux illuminance.</p>

<p>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. </p><p>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. </p><p>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.</p><p>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. </p><p>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. </p><p>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.</p> / Dissertation

The traditional approach to imaging system design places the sole burden of image formation on optical components. In contrast, a computational imaging system relies on a combination of optics and post-processing to produce the final image and/or output measurement. Therefore, the joint-optimization (JO) of the optical and the post-processing degrees of freedom plays a critical role in the design of computational imaging systems. The JO framework also allows us to incorporate task-specific performance measures to optimize an imaging system for a specific task. In this dissertation, we consider the design of computational imaging systems within a JO framework for two separate tasks: object reconstruction and iris-recognition. The goal of these design studies is to optimize the imaging system to overcome the performance degradations introduced by under-sampled image measurements. Within the JO framework, we engineer the optical point spread function (PSF) of the imager, representing the optical degrees of freedom, in conjunction with the post-processing algorithm parameters to maximize the task performance. For the object reconstruction task, the optimized imaging system achieves a 50% improvement in resolution and nearly 20% lower reconstruction root-mean-square-error (RMSE ) as compared to the un-optimized imaging system. For the iris-recognition task, the optimized imaging system achieves a 33% improvement in false rejection ratio (FRR) for a fixed alarm ratio (FAR) relative to the conventional imaging system. The effect of the performance measures like resolution, RMSE, FRR, and FAR on the optimal design highlights the crucial role of task-specific design metrics in the JO framework. We introduce a fundamental measure of task-specific performance known as task-specific information (TSI), an information-theoretic measure that quantifies the information content of an image measurement relevant to a specific task. A variety of source-models are derived to illustrate the application of a TSI-based analysis to conventional and compressive imaging (CI) systems for various tasks such as target detection and classification. A TSI-based design and optimization framework is also developed and applied to the design of CI systems for the task of target detection, it yields a six-fold performance improvement over the conventional imaging system at low signal-to-noise ratios.

<p>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. </p><p>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. </p><p>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. </p><p>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.</p> / Dissertation

<p>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.</p><p>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.</p><p>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.</p><p>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.</p> / Dissertation

Computational sensing utilizes non-conventional sampling mechanisms along with processing algorithms for accomplishing various sensing tasks. It provides additional flexibility in designing imaging or spectroscopic systems. This dissertation analyzes sampling and signal estimation techniques through three computational sensing systems to accomplish specific tasks. The first is thin long-wave infrared imaging systems through multichannel sampling. Significant reduction in optical system thickness is obtained over a conventional system by modifying conventional sampling mechanisms and applying reconstruction algorithms. In addition, an information theoretic analysis of sampling in conventional as well as multichannel imaging systems is also performed. The feasibility of performing multichannel sampling for imaging is demonstrated using an information theoretic metric. The second system is an application of the multichannel system for the design of compressive low-power video sensors. Two sampling schemes have been demonstrated that utilize spatial as well as temporal aliasing. The third system is a novel computational spectroscopic system for detecting chemicals that utilizes the surface plasmon resonances to encode information about the chemicals that are tested. / Dissertation

Miniature cameras play a key role in numerous imaging applications ranging from endoscopy and metrology inspection devices to smartphones and head-mount acquisition systems. However, due to the physical constraints, the imaging conditions, and the low quality of small optics, their imaging capabilities are limited in terms of the delivered resolution, the acquired depth of field, and the captured dynamic range. Computational imaging jointly addresses the imaging system and the reconstructing algorithms to bypass the traditional limits of optical systems and deliver better restorations for various applications. The scene is encoded into a set of efficient measurements which could then be computationally decoded to output a richer estimate of the scene as compared with the raw images captured by conventional imagers. In this dissertation, three task-based computational imaging techniques are developed to make low-quality miniature cameras capable of delivering realistic high-resolution reconstructions, providing full-focus imaging, and acquiring depth information for high dynamic range objects. For the superresolution task, a non-regularized direct superresolution algorithm is developed to achieve realistic restorations without being penalized by improper assumptions (e.g., optimizers, priors, and regularizers) made in the inverse problem. An adaptive frequency-based filtering scheme is introduced to upper bound the reconstruction errors while still producing more fine details as compared with previous methods under realistic imaging conditions. For the full-focus imaging task, a computational depth-based deconvolution technique is proposed to bring a scene captured by an ordinary fixed-focus camera to a full-focus based on a depth-variant point spread function prior. The ringing artifacts are suppressed on three levels: block tiling to eliminate boundary artifacts, adaptive reference maps to reduce ringing initiated by sharp edges, and block-wise deconvolution or depth-based masking to suppress artifacts initiated by neighboring depth-transition surfaces. Finally for the depth acquisition task, a multi-polarization fringe projection imaging technique is introduced to eliminate saturated points and enhance the fringe contrast by selecting the proper polarized channel measurements. The developed technique can be easily extended to include measurements captured under different exposure times to obtain more accurate shape rendering for very high dynamic range objects.

<p>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. </p><p>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.</p> / Dissertation

There is an unprecedented need in neuroscience and medical research for the precise imaging of individual neurons and their interconnectivity in an effort to achieve a more complete understanding of neurological illness and cognitive growth. While several imaging architectures successfully detect active neural tissue, fluorescent imaging through head-mounted microscopes is becoming a standard method of imaging neural circuitry in freely behaving animals. At Boston University, the Gardner Group developed a miniaturized, open-source, single-photon ‘finch-scope’ to spur rapid prototyping in head-mounted miniscope technology. While experimentally convenient, the finch-scope and other miniscope platforms are limited by their native depth of field and may only detect a thin layer of active neurons in a neurological volume. In this Master’s Thesis Project, I will investigate utilizing optical phase masks integrated in the Fourier plane of the finch-scope to invoke a less-diffractive Bessel point spread function. Next, I will experimentally justify the extended depth of field nature of these phase masks by imaging the axial profile of a 10μm fluorescent pinhole object with a modified finch-scope.