  Hyperspectral stimulated Raman scattering (SRS) microscopy is a powerful
label-free, chemical-specific technique for biomedical and mineralogical
imaging which can suffer from low signal-to-noise ratios due to requirements of
low input laser power or fast imaging, or from optical scattering and low
target concentration. Here, we demonstrate a deep learning neural net model and
unsupervised machine-learning algorithm for rapid and automatic de-noising and
segmentation of SRS images based on a ten layer convolutional autoencoder:
UHRED (Unsupervised Hyperspectral Resolution Enhancement and De-noising). UHRED
is trained in an unsupervised manner using only a single (one-shot)
hyperspectral image, with no requirements for training on high quality (ground
truth) labelled data sets or images. Importantly, although we illustrate this
method using SRS, the hyperspectral index (signal as a function of a laser
parameter) may be any imaging modality such as harmonic generation, linear
and/or nonlinear fluorescence, CARS, Pump-Probe, Thermal Lensing and
Cross-Phase microscopy. UHRED significantly enhances SRS image contrast by
de-noising the extracted Raman spectra at every image pixel. Applying a k-means
clustering algorithm provides automatic, unsupervised image segmentation based
on Raman vibrational spectra of the sample constituents, yielding intuitive
chemical species maps, as we demonstrate for the case of a complex lithium ore
sample.
