Michael Mattheiss

Bio: Michael Mattheiss is an academic researcher from United States Army Research Laboratory. The author has contributed to research in topics: Holography & Undersampling. The author has an hindex of 2, co-authored 2 publications receiving 106 citations. Previous affiliations of Michael Mattheiss include University of Maryland, College Park.

Millimeter-wave compressive holography.

Abstract: We 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.

Sparse sampling and enhanced axial resolution in millimeter-wave holographic imaging

Abstract: This paper describes an active millimeter-wave (MMW) holographic imaging system used for the study of compressive measurement for concealed weapons detection. We record a digitized on-axis, Gabor hologram using a single pixel incoherent receiver that is translated at the detector plane to form an image composite. Capturing measurements in the MMW regime can be costly since receiver circuits are expensive and scanning systems can be plagued by their long data acquisition times. Thus, we leverage recent advances in compressive sensing with a traditional holographic method in order to estimate a 3D (x,y,z) object distribution from a 2D recorded image composite. To do this, we minimize a convex quadratic function using total variation (TV) regularization. Gabor holograms are recorded of semi-transparent objects, in the MMW, mimicking weapons and other objects. We present preliminary results of 3D reconstructions of objects at various depths estimated from a 2D recorded hologram. We compare backpropagation results with our decompressive inference algorithm. A possible application includes remote concealed weapons detection at security checkpoints.

Dictionary Learning

Abstract: We describe methods for learning dictionaries that are appropriate for the representation of given classes of signals and multisensor data. We further show that dimensionality reduction based on dictionary representation can be extended to address specific tasks such as data analy sis or classification when the learning includes a class separability criteria in the objective function. The benefits of dictionary learning clearly show that a proper understanding of causes underlying the sensed world is key to task-specific representation of relevant information in high-dimensional data sets.

Metamaterial Apertures for Computational Imaging

Abstract: By leveraging metamaterials and compressive imaging, a low-profile aperture capable of microwave imaging without lenses, moving parts, or phase shifters is demonstrated. This designer aperture allows image compression to be performed on the physical hardware layer rather than in the postprocessing stage, thus averting the detector, storage, and transmission costs associated with full diffraction-limited sampling of a scene. A guided-wave metamaterial aperture is used to perform compressive image reconstruction at 10 frames per second of two-dimensional (range and angle) sparse still and video scenes at K-band (18 to 26 gigahertz) frequencies, using frequency diversity to avoid mechanical scanning. Image acquisition is accomplished with a 40:1 compression ratio.

Compressive holography

Abstract: Compressive 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.

Metamaterial apertures for coherent computational imaging on the physical layer.

Abstract: We introduce the concept of a metamaterial aperture, in which an underlying reference mode interacts with a designed metamaterial surface to produce a series of complex field patterns. The resonant frequencies of the metamaterial elements are randomly distributed over a large bandwidth (18-26 GHz), such that the aperture produces a rapidly varying sequence of field patterns as a function of the input frequency. As the frequency of operation is scanned, different subsets of metamaterial elements become active, in turn varying the field patterns at the scene. Scene information can thus be indexed by frequency, with the overall effectiveness of the imaging scheme tied to the diversity of the generated field patterns. As the quality (Q-) factor of the metamaterial resonators increases, the number of distinct field patterns that can be generated increases-improving scene estimation. In this work we provide the foundation for computational imaging with metamaterial apertures based on frequency diversity, and establish that for resonators with physically relevant Q-factors, there are potentially enough distinct measurements of a typical scene within a reasonable bandwidth to achieve diffraction-limited reconstructions of physical scenes.

Metamaterial microwave holographic imaging system

Abstract: We demonstrate a microwave imaging system that combines advances in metamaterial aperture design with emerging computational imaging techniques. The flexibility inherent to guided-wave, complementary metamaterials enables the design of a planar antenna that illuminates a scene with dramatically varying radiation patterns as a function of frequency. As frequency is swept over the K-band (17.5–26.5 GHz), a sequence of pseudorandom radiation patterns interrogates a scene. Measurements of the return signal versus frequency are then acquired and the scene is reconstructed using computational imaging methods. The low-cost, frequency-diverse static aperture allows three-dimensional images to be formed without mechanical scanning or dynamic beam-forming elements. The metamaterial aperture is complementary to a variety of computational imaging schemes, and can be used in conjunction with other sensors to form a multifunctional imaging platform. We illustrate the potential of multisensor fusion by integrating an infrared structured-light and optical image sensor to accelerate the microwave scene reconstruction and to provide a simultaneous visualization of the scene.