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title:
Compressed sensing for ill-posed problems: recovery principles and accuracy estimates |
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name:
Herrholz |
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first name:
Evelyn
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location/conference:
cssip10
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PRESENTATION-link:
http://www.dfg-spp1324.de/nuhagtools/event/dateien/talks_cssip/herrholz.pdf |
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abstract:
This talk is concerned with compressive sampling strategies and sparse recovery principles for linear and ill-posed problems. We provide compressed measurement models and accuracy estimates for sparse approximations of the solution of the underlying inverse problem. Thereby, we rely on Tikhonov variational and constraint optimization formulations. One essential difference to the classical compressed sensing framework is the incorporation of a joint sparsity measure allowing the treatment of infinite dimensional reconstruction spaces. The theoretical results are furnished with a numerical experiment. |
