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Raman microscopy is a valuable tool for detecting physical and chemicalproperties of a sample material. When probing nanomaterials or nanocompositesthe spatial resolution of Raman microscopy is not always adequate as it islimited by the optical diffraction limit. Numerical post-processing withsuper-resolution algorithms provides a means to enhance resolution and can bestraightforwardly applied. The aim of this work is to present interior-pointleast squares (IPLS) as a powerful tool for super-resolution in Raman imagingthrough constrained optimisation. IPLS's potential for super-resolution isillustrated on numerically generated test images. Its resolving power isdemonstrated on Raman spectroscopic data of a polymer nanowire sample.Comparison to AFM data of the same sample substantiates that the presentedmethod is a promising technique for analysing nanomaterial samples.

Sub-micron spatial resolution in far-field Raman imaging via positivity constrained super-resolution