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David J. Brady

Researcher at University of Arizona

Publications -  718
Citations -  22957

David J. Brady is an academic researcher from University of Arizona. The author has contributed to research in topics: Coded aperture & Holography. The author has an hindex of 74, co-authored 703 publications receiving 20285 citations. Previous affiliations of David J. Brady include Georgia Institute of Technology & University of Illinois at Urbana–Champaign.

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Single disperser design for coded aperture snapshot spectral imaging

TL;DR: A single disperser spectral imager is presented that exploits recent theoretical work in the area of compressed sensing to achieve snapshot spectral imaging and can be used to capture spatiospectral information of a scene that consists of two balls illuminated by different light sources.
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Single-shot compressive spectral imaging with a dual-disperser architecture

TL;DR: A single-shot spectral imaging approach based on the concept of compressive sensing with primary features of two dispersive elements, arranged in opposition and surrounding a binary-valued aperture code, which results in easily-controllable, spatially-varying, spectral filter functions with narrow features.
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Compressive Coded Aperture Spectral Imaging: An Introduction

TL;DR: The remarkable advantage of CASSI is that the entire data cube is sensed with just a few FPA measurements and, in some cases, with as little as a single FPA shot.
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Metamaterial Apertures for Computational Imaging

TL;DR: By leveraging metamaterials and compressive imaging, a low-profile aperture capable of microwave imaging without lenses, moving parts, or phase shifters is demonstrated and 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.
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Adaptive optical networks using photorefractive crystals.

TL;DR: The capabilities of photorefractive crystals as media for holographic interconnections in neural networks are examined and optical architectures for implementing various neural schemes are described.