Self-optimized spectral distance for low-light high-throughput Raman hyperspectral imaging

Raman hyperspectral imaging is a powerful technique for probing the intrinsic properties of samples by combining vibrational spectroscopy with spatial imaging. Despite its potential, the inherently weak Raman scattering signal typically necessitates prolonged acquisition times or high-power lasers, thereby limiting its efficiency and broader applicability. Here we present a computational method for facilitating Raman imaging under challenging conditions. We propose that even low-quality measurements-acquired with short integration times or low-power lasers-still contain sufficient information of Raman spectra. To this end, an unsupervised learning-based method, self-optimized spectral distance (SSD), is developed to reconstruct Raman images directly from 'noisy' measurements. By eliminating the dependence on large-scale training datasets, long imaging times and high-energy lasers, SSD helps to advance high-throughput Raman imaging. In diverse applications, including cellular structure analysis, microparticle detection and pharmaceutical ingredient identification, SSD achieves high imaging quality while reducing acquisition time and excitation power at least one order of magnitude.