# Browsing by Author "Brady, David J"

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Item Open Access Adaptive millimeter-wave synthetic aperture imaging for compressive sampling of sparse scenes.(Opt Express, 2014-06-02) Mrozack, Alex; Heimbeck, Martin; Marks, Daniel L; Richard, Jonathan; Everitt, Henry O; Brady, David JWe apply adaptive sensing techniques to the problem of locating sparse metallic scatterers using high-resolution, frequency modulated continuous wave W-band RADAR. Using a single detector, a frequency stepped source, and a lateral translation stage, inverse synthetic aperture RADAR reconstruction techniques are used to search for one or two wire scatterers within a specified range, while an adaptive algorithm determined successive sampling locations. The two-dimensional location of each scatterer is thereby identified with sub-wavelength accuracy in as few as 1/4 the number of lateral steps required for a simple raster scan. The implications of applying this approach to more complex scattering geometries are explored in light of the various assumptions made.Item Open Access Analytic-domain lens design with proximate ray tracing.(J Opt Soc Am A Opt Image Sci Vis, 2010-08-01) Zheng, Nan; Hagen, Nathan; Brady, David JWe have developed an alternative approach to optical design which operates in the analytical domain so that an optical designer works directly with rays as analytical functions of system parameters rather than as discretely sampled polylines. This is made possible by a generalization of the proximate ray tracing technique which obtains the analytical dependence of the rays at the image surface (and ray path lengths at the exit pupil) on each system parameter. The resulting method provides an alternative direction from which to approach system optimization and supplies information which is not typically available to the system designer. In addition, we have further expanded the procedure to allow asymmetric systems and arbitrary order of approximation, and have illustrated the performance of the method through three lens design examples.Item Open Access Coded aperture compressive temporal imaging.(Opt Express, 2013-05-06) Llull, Patrick; Liao, Xuejun; Yuan, Xin; Yang, Jianbo; Kittle, David; Carin, Lawrence; Sapiro, Guillermo; Brady, David JWe use mechanical translation of a coded aperture for code division multiple access compression of video. We discuss the compressed video's temporal resolution and present experimental results for reconstructions of > 10 frames of temporal data per coded snapshot.Item Open Access Coding Strategies and Implementations of Compressive Sensing(2016) Tsai, Tsung-HanThis 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.

Item Open Access Coding Strategies for X-ray Tomography(2016) Holmgren, AndrewThis work focuses on the construction and application of coded apertures to compressive X-ray tomography. Coded apertures can be made in a number of ways, each method having an impact on system background and signal contrast. Methods of constructing coded apertures for structuring X-ray illumination and scatter are compared and analyzed. Apertures can create structured X-ray bundles that investigate specific sets of object voxels. The tailored bundles of rays form a code (or pattern) and are later estimated through computational inversion. Structured illumination can be used to subsample object voxels and make inversion feasible for low dose computed tomography (CT) systems, or it can be used to reduce background in limited angle CT systems.

On the detection side, coded apertures modulate X-ray scatter signals to determine the position and radiance of scatter points. By forming object dependent projections in measurement space, coded apertures multiplex modulated scatter signals onto a detector. The multiplexed signals can be inverted with knowledge of the code pattern and system geometry. This work shows two systems capable of determining object position and type in a 2D plane, by illuminating objects with an X-ray `fan beam,' using coded apertures and compressive measurements. Scatter tomography can help identify materials in security and medicine that may be ambiguous with transmission tomography alone.

Item Open Access Compressive holography of diffuse objects.(Appl Opt, 2010-12-01) Choi, Kerkil; Horisaki, Ryoichi; Hahn, Joonku; Lim, Sehoon; Marks, Daniel L; Schulz, Timothy J; Brady, David JWe propose an estimation-theoretic approach to the inference of an incoherent 3D scattering density from 2D scattered speckle field measurements. The object density is derived from the covariance of the speckle field. The inference is performed by a constrained optimization technique inspired by compressive sensing theory. Experimental results demonstrate and verify the performance of our estimates.Item Open Access Compressive holography.(2012) Lim, Se HoonCompressive holography estimates images from incomplete data by using sparsity priors. Compressive holography combines digital holography and compressive sensing. Digital holography consists of computational image estimation from data captured by an electronic focal plane array. Compressive sensing enables accurate data reconstruction by prior knowledge on desired signal. Computational and optical co-design optimally supports compressive holography in the joint computational and optical domain. This dissertation explores two examples of compressive holography : estimation of 3D tomographic images from 2D data and estimation of images from under sampled apertures. Compressive holography achieves single shot holographic tomography using decompressive inference. In general, 3D image reconstruction suffers from underdetermined measurements with a 2D detector. Specifically, single shot holographic tomography shows the uniqueness problem in the axial direction because the inversion is ill-posed. Compressive sensing alleviates the ill-posed problem by enforcing some sparsity constraints. Holographic tomography is applied for video-rate microscopic imaging and diffuse object imaging. In diffuse object imaging, sparsity priors are not valid in coherent image basis due to speckle. So incoherent image estimation is designed to hold the sparsity in incoherent image basis by support of multiple speckle realizations. High pixel count holography achieves high resolution and wide field-of-view imaging. Coherent aperture synthesis can be one method to increase the aperture size of a detector. Scanning-based synthetic aperture confronts a multivariable global optimization problem due to time-space measurement errors. A hierarchical estimation strategy divides the global problem into multiple local problems with support of computational and optical co-design. Compressive sparse aperture holography can be another method. Compressive sparse sampling collects most of significant field information with a small fill factor because object scattered fields are locally redundant. Incoherent image estimation is adopted for the expanded modulation transfer function and compressive reconstruction.Item Open Access Compressive sensing and adaptive sampling applied to millimeter wave inverse synthetic aperture imaging(Optics Express, 2017-02-06) Zhu, Ruoyu; Richard, Jonathan T; Brady, David J; Marks, Daniel L; Everitt, Henry O© 2017 Optical Society of America.In order to improve speed and efficiency over traditional scanning methods, a Bayesian compressive sensing algorithm using adaptive spatial sampling is developed for single detector millimeter wave synthetic aperture imaging. The application of this algorithm is compared to random sampling to demonstrate that the adaptive algorithm converges faster for simple targets and generates more reliable reconstructions for complex targets.Item Open Access Compressive video sensors using multichannel imagers.(Appl Opt, 2010-04-01) Shankar, Mohan; Pitsianis, Nikos P; Brady, David JWe explore the possibilities of obtaining compression in video through modified sampling strategies using multichannel imaging systems. The redundancies in video streams are exploited through compressive sampling schemes to achieve low power and low complexity video sensors. The sampling strategies as well as the associated reconstruction algorithms are discussed. These compressive sampling schemes could be implemented in the focal plane readout hardware resulting in drastic reduction in data bandwidth and computational complexity.Item Open Access Computational Optical Imaging Systems for Spectroscopy and Wide Field-of-View Gigapixel Photography(2013) Kittle, David S.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.

Item Open Access Computational spectral microscopy and compressive millimeter-wave holography(2010) Fernandez, Christy AnnThis dissertation describes three computational sensors. The first sensor is a scanning multi-spectral aperture-coded microscope containing a coded aperture spectrometer that is vertically scanned through a microscope intermediate image plane. The spectrometer aperture-code spatially encodes the object spectral data and nonnegative

least squares inversion combined with a series of reconfigured two-dimensional (2D spatial-spectral) scanned measurements enables three-dimensional (3D) (x, y, λ) object estimation. The second sensor is a coded aperture snapshot spectral imager that employs a compressive optical architecture to record a spectrally filtered projection

of a 3D object data cube onto a 2D detector array. Two nonlinear and adapted TV-minimization schemes are presented for 3D (x,y,λ) object estimation from a 2D compressed snapshot. Both sensors are interfaced to laboratory-grade microscopes and

applied to fluorescence microscopy. The third sensor is a millimeter-wave holographic imaging system that is used to study the impact of 2D compressive measurement on 3D (x,y,z) data estimation. Holography is a natural compressive encoder since a 3D

parabolic slice of the object band volume is recorded onto a 2D planar surface. An adapted nonlinear TV-minimization algorithm is used for 3D tomographic estimation from a 2D and a sparse 2D hologram composite. This strategy aims to reduce scan time costs associated with millimeter-wave image acquisition using a single pixel receiver.

Item Open Access Generalized sampling using a compound-eye imaging system for multi-dimensional object acquisition.(Opt Express, 2010-08-30) Horisaki, Ryoichi; Choi, Kerkil; Hahn, Joonku; Tanida, Jun; Brady, David JIn this paper, we propose generalized sampling approaches for measuring a multi-dimensional object using a compact compound-eye imaging system called thin observation module by bound optics (TOMBO). This paper shows the proposed system model, physical examples, and simulations to verify TOMBO imaging using generalized sampling. In the system, an object is modulated and multiplied by a weight distribution with physical coding, and the coded optical signal is integrated on to a detector array. A numerical estimation algorithm employing a sparsity constraint is used for object reconstruction.Item Open Access Identification of fluorescent beads using a coded aperture snapshot spectral imager.(Appl Opt, 2010-04-01) Cull, Christy Fernandez; Choi, Kerkil; Brady, David J; Oliver, TimWe apply a coded aperture snapshot spectral imager (CASSI) to fluorescence microscopy. CASSI records a two-dimensional (2D) spectrally filtered projection of a three-dimensional (3D) spectral data cube. We minimize a convex quadratic function with total variation (TV) constraints for data cube estimation from the 2D snapshot. We adapt the TV minimization algorithm for direct fluorescent bead identification from CASSI measurements by combining a priori knowledge of the spectra associated with each bead type. Our proposed method creates a 2D bead identity image. Simulated fluorescence CASSI measurements are used to evaluate the behavior of the algorithm. We also record real CASSI measurements of a ten bead type fluorescence scene and create a 2D bead identity map. A baseline image from filtered-array imaging system verifies CASSI's 2D bead identity map.Item Open Access Imaging through turbulence using compressive coherence sensing.(Opt Lett, 2010-03-15) Wagadarikar, Ashwin A; Marks, Daniel L; Choi, Kerkil; Horisaki, Ryoichi; Brady, David JPrevious studies have shown that the isoplanatic distortion due to turbulence and the image of a remote object may be jointly estimated from the 4D mutual intensity across an aperture. This Letter shows that decompressive inference on a 2D slice of the 4D mutual intensity, as measured by a rotational shear interferometer, is sufficient for estimation of sparse objects imaged through turbulence. The 2D slice is processed using an iterative algorithm that alternates between estimating the sparse objects and estimating the turbulence-induced phase screen. This approach may enable new systems that infer object properties through turbulence without exhaustive sampling of coherence functions.Item Open Access Implicit and Explicit Codes For Diffraction Tomography(2014) Mrozack, AlexanderDiffraction tomography is the attempt to estimate the scattering density of an object from measurements of a scattered coherent field. This work moves to overcome many of the constraints and limitations of the current state of the art. In general, these constraints present themselves as physical and cost limitations. The limitations ``encode" the data, giving rise to the title of this dissertation. Implicit coding is the encoding of the data by the acquisition system. For instance, coherent scatter is bound to be sampled on specific arcs in the Fourier space of the scattering density. Explicit coding is the choice of how the data is sampled within the implicit coding limitations. The beam patterns of an antenna may be optimized to better detect certain types of targets, or datasets may be subsampled if prior knowledge of the scene is introduced in some way.

We investigate both of these types of data coding, introduce a method for sampling a particular type of scene with high efficiency, and present strategies for overcoming a specific type of implicit data encoding which is detrimental to ``pure" image estimation known as speckle. The final chapter of this dissertation incorporates both implicit and explicit coding strategies, to demonstrate the importance of taking both into account for a new paradigm in diffraction tomography known as frequency diversity imaging. Frequency diversity imaging explicitly encodes coherent fields on the illumination wavelength. Combining this paradigm with speckle estimation requires a new way to evaluate the quality of explicit codes.

Item Open Access Improving Radar Imaging with Computational Imaging and Novel Antenna Design(2017) Zhu, RuoyuTraditional 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.

Item Open Access Millimeter-wave compressive holography.(Appl Opt, 2010-07-01) Cull, Christy Fernandez; Wikner, David A; Mait, Joseph N; Mattheiss, Michael; Brady, David JWe describe an active millimeter-wave holographic imaging system that uses compressive measurements for three-dimensional (3D) tomographic object estimation. Our system records a two-dimensional (2D) digitized Gabor hologram by translating a single pixel incoherent receiver. Two approaches for compressive measurement are undertaken: nonlinear inversion of a 2D Gabor hologram for 3D object estimation and nonlinear inversion of a randomly subsampled Gabor hologram for 3D object estimation. The object estimation algorithm minimizes a convex quadratic problem using total variation (TV) regularization for 3D object estimation. We compare object reconstructions using linear backpropagation and TV minimization, and we present simulated and experimental reconstructions from both compressive measurement strategies. In contrast with backpropagation, which estimates the 3D electromagnetic field, TV minimization estimates the 3D object that produces the field. Despite undersampling, range resolution is consistent with the extent of the 3D object band volume.Item Open Access Optical Design for Parallel Cameras(2020) Pang, WubinThe Majority of imaging systems require optical lenses to increase the light throughput as well as to form an isomorphic mapping. Advances in optical lenses improve observing power. However, as imaging resolution reaches about the magnitude of $10^8$ or higher, such as gigapixel cameras, the conventional monolithic lens architecture and processing routine is no longer sustainable due to the non-linearly increased optical size, weight, complexity and therefore the overall cost. The information efficiency measured by pixels per unit-cost drops drastically as the aperture size and field of view (FoV) march toward extreme values. On the one hand, reducing the up-scaled wavefront error to a fraction of wavelength requires more surfaces and more complex figures. On the other hand, the scheme of sampling 3-dimensional scenes with a single 2-dimensional aperture does not scale well, when the sampling space is extended. Correction for shift-varying sampling and non-uniform luminance aggravated by wide-field angles can easily lead to an explosion of the lens complexity.

Parallel cameras utilize multiple apertures and discrete focal planes to reduce camera complexity via the principle of divide and conquer. The high information efficiency of lenses with small aperture and narrow FoV is preserved. Also, modular design gives flexibility in configuration and reconfiguration, provides easy adaptation and inexpensive maintenance.

Multiscale lens design utilizes optical elements in various size scales. Large aperture optics collects light coherently, and small aperture optics enable efficient light processing. Monocentric multiscale (MMS) lenses exemplify this idea by adopting a multi-layered spherical lens as the front objective and an array of microcameras at the rear for segmenting and relaying the wide-field image onto disjoint focal planes. First generation as-constructed MMS lenses adopted Keplerian style, which features a real intermediate image surface. In this dissertation, we investigate another design style termed "Galilean", which eliminates the intermediate image surface, therefore leading to significantly reduced lens size and weight.

The FoV shape of a parallel camera is determined by the formation of the camera arrays. Arranging array cameras in myriad formations allows FoV to be captured in different shapes. This flexibility in FoV format arrangement facilitates customized camera applications and new visual experiences.

Parallel cameras can consist of dozens or even hundreds of imaging channels. Each channel requires an independent focusing mechanism for all in focus capture. The tight budget on packing space and expense desires small and inexpensive focusing mechanism. This dissertation addresses this problem with the voice coil motor (VCM) based focusing mechanism found on mobile platforms. We propose miniaturized optics in long focal length designs, thus reduces the traveling range of the focusing group, and enables universal focus.

Along the same line of building cost-efficient and small size lens systems, we explore ways of making thin lenses with low telephoto ratios. We illustrate a catadioptric design achieving a telephoto ratio of 0.35. The combination of high index material and meta-surfaces could push this value down to 0.18, as shown by one of our design examples.

Item Open Access Physical Designs in Artificial Neural Imaging(2022) Huang, QianArtificial 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.

Item Open Access Sampling and Signal Estimation in Computational Optical Sensors(2007-12-14) Shankar, MohanComputational 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.