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

TL;DR

This paper introduces an interior-point least squares (IPLS) super-resolution algorithm for Raman imaging, achieving sub-micron resolution beyond the diffraction limit, validated on nanomaterials with promising results.

Contribution

The paper presents IPLS as a novel constrained optimization method for super-resolution in Raman microscopy, enhancing spatial resolution in nanomaterial analysis.

Findings

IPLS achieves sub-micron resolution in Raman imaging.

Comparison with AFM confirms IPLS's effectiveness.

Numerical tests demonstrate IPLS's potential for nanomaterial analysis.

Abstract

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.