Spectral imaging is a ubiquitous tool in modern biochemistry. Despite acquiring dozens to thousands of spectralchannels, existing technology cannot capture spectral images at the same spatial resolution as structuralmicroscopy. Due to partial voluming and low light exposure, spectral images are often difficult to interpretand analyze. This highlights a need to upsample the low-resolution spectral image by using spatial informationcontained in the high-resolution image, thereby creating a fused representation with high specificity bothspatially and spectrally. In this paper, we propose a framework for the fusion of co-registered structural andspectral microscopy images to create super-resolved representations of spectral images. As a first application, wesuper-resolve spectral images of retinal tissue imaged with confocal laser scanning microscopy, by using spatialinformation from structured illumination microscopy. Second, we super-resolve mass spectroscopic images ofmouse brain tissue, by using spatial information from high-resolution histology images. We present a systematicvalidation of model assumptions crucial towards maintaining the original nature of spectra and the applicabilityof super-resolution. Goodness-of-fit for spectral predictions are evaluated through functional R~2 values, and thespatial quality of the super-resolved images are evaluated using normalized mutual information.
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