Machine learning based sparse estimation

Dr. Péter Kovács
Institute of Signal Processing (JKU Linz)
SAL Linz
Altenberger Strasse 69
4040 Linz
Dienstag, 28.07.2020
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In his Science Talk, Dr. Péter Kovács talks about "Machine learning based sparse estimation".

The concept of sparsity has been studied for nearly a century, but it revealed its true potential nature due to the advent of compressed sensing in 2006. The theory suggests that a sparse signal can be reconstructed by exploiting only a few measured values, which can go below the fundamental Nyquist sampling rate. Another interpretation of sparsity can be given by Occam's razor: "among competing representations that predict equally well, the one with the fewest number of components should be selected". In this project, we develop machine learning algorithms to utilize sparsity in various signal processing applications. As a case study, we consider the problem of thermographic image reconstruction in non-destructive material testing.