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Compressed sensing is a promising approach for significantly reducing the number of measurements in photoacoustic tomography (or other limited data imaging problems) while preserving its high spatial resolution. In this talk we present a new sparse recovery framework for recovering the photoacoustic source from compressive measurements. Results with simulated as well as experimental data are given. (Joint work with Linh Nguyen, Michael Sandbichler, Thomas Berer, Johannes Bauer-Marschallinger, Peter Burgholzer)
This presentation is part of Minisymposium “MS7 - Limited data problems in imaging (2 parts)”
organized by: Bernadette Hahn (University of Würzburg) , Gaël Rigaud (Saarland University) , Jürgen Frikel (OTH Regensburg) .