Browsing by Author "Giljum, Anthony T."

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    On-the-fly compressive single-pixel foveation using the STOne transform
    (Optica Publishing Group, 2022) Giljum, Anthony T.; Giljum, Anthony T.; Kelly, Kevin F.
    Compressive imaging allows one to sample an image below the Nyquist rate yet still accurately recover it from the measurements by solving an L1 optimization problem. The L1 solvers, however, are iterative and can require significant time to reconstruct the original signal. Intuitively, the reconstruction time can be reduced by reconstructing fewer total pixels. The human eye reduces the total amount of data it processes by having a spatially varying resolution, a method called foveation. In this work, we use foveation to achieve a 4x improvement in L1 compressive sensing reconstruction speed for hyperspectral images and video. Unlike previous works, the presented technique allows the high-resolution region to be placed anywhere in the scene after the subsampled measurements have been acquired, has no moving parts, and is entirely non-adaptive.
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    Versatile Compressive Sensing: Extensions of Random Projections for Imaging and Machine Vision
    (2021-11-17) Giljum, Anthony T.; Kelly, Kevin F.
    Compressive imaging is a versatile approach to sensing that offers the potential to dramatically reduce data acquisition time by multiplexing the scene with a spatial light modulator below than the Nyquist rate. The trade-off, however, is a high reconstruction time that limits real-time applications. The first half of this thesis will focus on multiplexed single-pixel imaging techniques. To this end, I will give a method for speeding up image reconstruction by foveating the image anywhere in the scene with regions of interest determined entirely after measurement acquisition. The technique is then extended to hyperspectral video and neural network reconstruction. Then, I will describe the use of shallow neural networks to map from random, task-insensitive measurements to highly targeted, task-specific measurements without sacrificing reconstruction capability. This work improves single-pixel object classification accuracies even when the object is against a complex, unknown background. I will then demonstrate an extension of these ideas to hyperspectral video in sum-frequency generation microscopy by measuring the localized blue shift in a sample of CO on a Pt surface as the electrochemical potential is swept. The second half of this thesis focuses on focal-plane array imaging. First, a diffraction limited, wide field-of-view hyperspectral microscope design will be shown. The proposed system is capable of acquiring images at 3 megapixels with over 100 spectral bands in under 30 seconds through spectrally multiplexed illumination. As an example application, the microscope is used for reflection contrast spectroscopy to measure the thickness of very thin graphene and MoS2. Finally, a novel snapshot hyperspectral camera design is shown, which uses a random Bayer filter and invertible blurring to compressively capture the entire hyperspectral datacube at the camera's pixel resolution in a single image. The proposed snapshot camera is compact and requires no moving parts or spatial-light modulators