  Raman microscopy is a valuable tool for detecting physical and chemical
properties of a sample material. When probing nanomaterials or nanocomposites
the spatial resolution of Raman microscopy is not always adequate as it is
limited by the optical diffraction limit. Numerical post-processing with
super-resolution algorithms provides a means to enhance resolution and can be
straightforwardly applied. The aim of this work is to present interior-point
least squares (IPLS) as a powerful tool for super-resolution in Raman imaging
through constrained optimisation. IPLS's potential for super-resolution is
illustrated on numerically generated test images. Its resolving power is
demonstrated on Raman spectroscopic data of a polymer nanowire sample.
Comparison to AFM data of the same sample substantiates that the presented
method is a promising technique for analysing nanomaterial samples.
