报告题目: |
Faster Imaging with Sparse Sampling |
报告人: |
Zhipei Liang |
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Ph.D.
Professor of Department of Electrical and Computer Engineering, and Beckman Institute for Advanced Science and Technology at the University of Illinois at Urbana-Champaign, USA
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报告时间: |
2012-01-10 15:40 |
报告地点: |
Room B323, Medical Science Building |
主办单位: |
医学院 |
简介: |
Conventional imaging methods are based on Shannon sampling theory. As such, the number of Nyquist samples (or measurements) grows exponentially as the physical dimension of the underlying imaging problem increases (the so-called curse of dimensionality), rendering it difficult to achieve high resolution for high-dimensional imaging. Sub-Nyquist sampling is possible for sparse and/or partially separable signals and is providing a powerful way to speed up various imaging experiments. This talk will provide an overview of recent sparse sampling methods based on compressed sensing theory and partial separable functions theory. An emphasis will be placed on discussing the issues of image reconstruction from sub-Nyquist data with sparsity and partial separability constraints and demonstrating their potential applications. |
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