Optimal trade-off filters for compressed Raman classification and spectrum reconstruction.

Compressed Raman spectroscopy is a promising technique for fast chemical analysis. In particular, classification between species with known spectra can be performed with measures acquired through a few binary filters. Moreover, it is possible to reconstruct spectra by using enough filters. As classification and reconstruction are competing, designing filters allowing one to perform both tasks is challenging. To tackle this problem, we propose to build optimal trade-off filters, i.e., filters so that there exist no filters achieving better performance in both classification and reconstruction. With this approach, users get an overview of reachable performance and can choose the trade-off most fitting their application.