Collaborative Low-Rank Matrix Approximation-Assisted Fast Hyperspectral Raman Imaging and Tip-Enhanced Raman Spectroscopic Imaging

Fast acquisition of Raman images is essential for accurately characterizing the analytes' information. In this paper, we developed a collaborative low-rank matrix approximation method for fast hyperspectral Raman imaging as well as tip-enhanced Raman spectroscopy (TERS) imaging. This method combines high signal-to-noise ratio (SNR) data with the target data to perform collaborative singular value decomposition. The high-quality reference data can impose constraints on factorization, which will force its components to approximate the true signal or noise components. The simulation demonstrated that this method offers state-of-the-art signal extraction performance and, thus, can be used to accelerate data acquisition. Specifically, the results indicate that the CLRMA can largely decrease the root-mean-square error by 20.92–54.12% compared with the baseline method of our previous study. We then applied this method to the fast TERS imaging of a Au/Pd bimetallic surface and significantly decreased the integration time down to 0.1 s/pixel, which is about 10 times faster than that of conventional experiments. High-SNR TERS spectra and clear TERS images that are well consistent with scanning tunneling microscopy (STM) images can be obtained even under such a weak signal condition. We further applied this method to the fast Raman imaging of HeLa cells and obtained clear Raman images at a short integration time of 2 s/line, which is about 5 times faster than that of conventional experiments. This method offers a promising tool for TERS imaging as well as conventional Raman imaging where fast data acquisition is required.