ABSTRACT
This work introduces hyper-resolution (HyRes), a numerical approach for spatial resolution enhancement that combines hyperspectral unmixing and super-resolution image restoration (SRIR). HyRes yields a substantial increase in spatial resolution of Raman spectroscopy while simultaneously preserving the undistorted spectral information. The resolving power of this technique is demonstrated on Raman spectroscopic data from a polymer nanowire sample. Here, we demonstrate an achieved resolution of better than 14 nm, a more than eightfold improvement on single-channel image-based SRIR and 25× better than regular far-field Raman spectroscopy, and comparable to near-field probing techniques.
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 optimization. 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 atomic force microscopy data of the same sample substantiates that the presented method is a promising technique for analyzing nanomaterial samples.
