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题目： 压缩频谱和层析图像感知 Compressive Spectral and Tomographic Image Sensing
报告人： Gonzalo R. Arce，Charles Black Evans教授，电子与计算机工程，特拉华大学
Dr. Gonzalo R. Arce is the Charles Black Evans Professor in the Electrical and Computer Engineering Department at the University of Delaware. He holds the JPMorgan Chase Faculty Scholar Professorship in the Institute of Financial Services Analytics. His fields of interest lie in computational imaging, nonlinear signal processing, and the analysis and processing of high-dimensional data. He is the author of four textbooks in the areas of computational imaging and statistical signal processing. He received the 2010-2011 Nokia-Fulbright Distinguished Chair in Information and Communications Technologies. He is a Fellow of the IEEE and of the Center for Advanced Studies at the University of Delaware. His work has been supported by ARO, ONR, ARL, NSF, and several industrial organizations.
Coded aperture compressive imaging is described for two important imaging modalities: Spectral and Tomographic imaging. Both capture 3-Dimensional data cubes with just one or a few 2-Dimensional detector array measurements. The rich theory of compressive sensing is then used to effectively reconstruct the 3D information of interest from the set of underdetermined measurements. Notably, the coded apertures used in the measurements play a key role in such imaging systems. This talk describes the underlying imaging architectures and the intimate link between the coded apertures used in the imaging systems and the fundamental principles used in compressive sensing.