Machine learning based sparse estimation
Institute of Signal Processing (JKU Linz)
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.