Data-driven signal-to-noise enhancement in scattering near-field infrared microscopy.

Abstract

This study introduces a machine-learning approach to enhance signal-to-noise ratios in scattering-type scanning near-field optical microscopy (s-SNOM). While s-SNOM offers a high spatial resolution, its effectiveness is often hindered by low signal levels, particularly in weakly absorbing samples. To address these challenges, we utilize a data-driven "patch-based" machine learning reconstruction method, incorporating modern generative adversarial neural networks (CycleGANs) for denoising s-SNOM images. This method allows for flexible reconstruction of images of arbitrary sizes, a critical capability given the variable nature of scanned sample areas in point-scanning probe-based microscopies. The CycleGAN model is trained on unpaired sets of images captured at both rapid and extended acquisition times, thereby modeling instrument noise while preserving essential topographical and molecular information. The results show significant improvements in image quality, as indicated by higher structural similarity index and peak signal-to-noise ratio values, comparable to those obtained from images captured with four times the integration time. This method not only enhances image quality but also has the potential to reduce the overall data acquisition time, making high-resolution s-SNOM imaging more feasible for a wide range of biological and materials science applications.