Abstract
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.
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