20130321
Compressed Sensing is a novel research area, which was introduced in 2006, and since then has already become a key concept in various areas of applied mathematics. It predicts that highdimensional signals, which allow a sparse representation by a suitable basis/frame, can be recovered from highly incomplete linear measurements by using efficient algorithms. In this talk, after first introducing this methodology, we discuss its application to imaging science. We then analyze both theoretically and numerically how this methodology can be utilized to solve the geometric separation problem in modern imaging, namely to separate an image into morphologically distinct components.
This talk is provided together with the department of mathematics.
Category: CE SeminarTechnische Universität Darmstadt
Graduate School CE
Dolivostraße 15
D64293 Darmstadt

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