Sparsity and compressive sensing in inverse problems
Neubrandenburg University of Applied Sciences, Germany
This talk is concerned with important topics in the context of sparse recovery and compressive sensing for inverse and ill-posed problems. In the first part of the talk we discuss recovery concepts for linear as well as for nonlinear inverse problems for frame-based expansions and mixed and/or joint sparsity constraints. One special focus is on the development of numerically efficient schemes. Then, in the second part, we investigate the incomplete/compressed data scenario for inverse problems. We show, that the developed sparse recovery schemes can be used quite nicely to solve an inverse problem from compressively sampled data. To illustrate the proposed machinery, we present a number of interesting examples that fit within the scenario.
ISSN 1611 - 4086 | © IKM 2015