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Emily King
Hausdorff Center for Mathematics
Shearlet-Based Inpainting
Monday, March 25 3pm, 646 PGH
Abstract
An issue in data analysis is that of incomplete data, for example a
photograph with scratches or seismic data collected with fewer than
necessary sensors. There exists a unified approach to solving this problem
and that of data separation: namely, minimizing the norm of the analysis
(rather than synthesis) coefficients with respect to particular
frame(s).There have been a number of successful applications of this
method recently. Analyzing this method using the concept of clustered
sparsity leads to theoretical bounds and results, which will be presented.
Furthermore, necessary conditions for the frames to lead to sufficiently
good solutions will be shown, and this theoretical framework will be use
to show that shearlets are able to inpaint larger gaps than wavelets.
Finally, the results of numerical experiments comparing this approach to
inpainting to others will be presented.
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Last modified: April 08 2016 - 07:21:37