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Prof. Henry Arguello (Fullbright Visiting Prof in EE): “Codification Design in Compressive Imaging”
June 3 @ 4:30 pm - 5:30 pm
Location: Packard 101
Title: Codification Design in Compressive Imaging
Abstract: Compressive imaging enables faster acquisitions by capturing coded projections of the scenes. Codification elements used in compressive imaging systems include lithographic masks, gratings and micro-polarizers, which sense spatial, spectral, and temporal data. Codification plays a key role in compressive imaging as it determines the number of projections needed for correct reconstruction. In general, random coding patterns are sufficient for accurate reconstruction. Still, more recent studies have shown that code design not only yields to improved image reconstructions, but it can also reduce the number of required projections. This talk covers different tools for codification design in compressive imaging, such as the restricted isometry property, geometric and deep learning approaches. Applications in compressive spectral video, compressive X-ray computed tomography, and seismic acquisition will be also discussed.
Bio: Henry Arguello is a Professor of the Computer Science Department at Universidad Industrial de Santander, Bucaramanga, Colombia, where he leads the High Dimensional Signal Processing research group. He is currently a Fulbright visiting professor at the Electrical Engineering Department, Stanford University. Professor Arguello received the Master’s degree in Electrical Engineering from Universidad Industrial de Santander in 2003 and the Ph.D. degree in Electrical and Computer Engineering from the University of Delaware in 2013. He is a Senior Member of the IEEE and the Optical Society of America, and the President of the Signal Processing Chapter of IEEE Colombia Section. His research interests include statistical signal processing, high dimensional signal coding and processing, optical and computational imaging, optical coded aperture design, compressed sensing, hyperspectral imaging and numerical optimization using stochastic algorithms.