Compressed Raman method combining classification and estimation of spectra with optimized binary filters

Compressed Raman methods allow classification between known chemical species with only a few measurements through binary filters. We propose a methodology for binary filter optimization, in which filters are modified at each pixel so that classification can still be achieved pixel by pixel with a few measurements acquired in parallel, while retaining the ability to reconstruct a full spectrum when combining measurements from several pixels. This approach is robust to intensity variations between pixels. It relies on a generalized Bhattacharyya bound and on the Cramér-Rao bound to tailor filters with optimized performance.