TY  - GEN
AV  - public
TI  - TomoPIV meets Compressed Sensing
Y1  - 2009///
T3  - IWR-Preprints
ID  - heidok9760
KW  - compressed sensing 
KW  -  underdetermined systems of linear equations 
KW  -  positivity constraints in ill-posed problems 
KW  -  sparsest solution 
KW  -  TomoPIV
A1  - Petra, Stefania
A1  - Schnörr, Christoph
UR  - https://archiv.ub.uni-heidelberg.de/volltextserver/9760/
N2  - We study the discrete tomography problem in Experimental Fluid Dynamics - Tomographic Particle Image Velocimetry (TomoPIV) - from the viewpoint of compressed sensing (CS). The CS theory of recoverability and stability of sparse solutions to underdetermined linear inverse problems has rapidly evolved during the last years. We show that all currently available CS concepts predict an extremely poor worst case performance, and a low expected performance of the TomoPIV measurement system, indicating why low particle densities only are currently used by engineers in practice. Simulations demonstrate however that slight random perturbations of the TomoPIV measurement matrix considerably boost both worst-case and expected reconstruction performance. This finding is interesting for CS theory and for the design of TomoPIV measurement systems in practice.
ER  -