Abstract
A new super-resolution image restoration confocal Raman microscopy method (SRIR-RAMAN) is proposed for improving the spatial resolution of confocal Raman microscopy. This method can recover the lost high spatial frequency of the confocal Raman microscopy by using Poisson-MAP super-resolution imaging restoration, thereby improving the spatial resolution of confocal Raman microscopy and realizing its super-resolution imaging. Simulation analyses and experimental results indicate that the spatial resolution of SRIR-RAMAN can be improved by 65% to achieve 200 nm with the same confocal Raman microscopy system. This method can provide a new tool for high spatial resolution micro-probe structure detection in physical chemistry, materials science, biomedical science and other areas.
Highlights
Confocal Raman microscopy (CRM) is widely used as an important tool in frontier areas such as physical chemistry, materials science, biomedical science [1,2,3,4,5,6,7], because of its micro-spectral optical section detection capability
The pinhole size of conventional confocal Raman microscopy has to be a few hundred microns to ensure the sensitivity of micro-spectrum detection, so it restricts the improvement of CRM spatial resolution
To meet the urgent needs of improving the CRM spatial resolution, we propose a super-resolution image restoration confocal Raman microscopy method (SRIR-RAMAN) with high spatial resolution in this paper, which combines the super-resolution image restoration technology and CRM
Summary
Confocal Raman microscopy (CRM) is widely used as an important tool in frontier areas such as physical chemistry, materials science, biomedical science [1,2,3,4,5,6,7], because of its micro-spectral optical section detection capability. TERS makes the spatial resolution of Raman spectroscopy reach at the nanometer level, but it requires that the distance between the AFM tip and specimen to be at nanometer scale during image processing It reduces the detection efficiency and cannot realize tomography imaging, with the result that the requirements for the spectrum detection of internal information in biology, materials science and other fields could not be met. The method uses a Maximum a posterior algorithm based on the Poisson-Markov model (MPMAP) [19] to increase the capability of noise immunity and the convergence speed, and conducts super-resolution image restoration on single Raman imaging at the same time, and thereby achieving high CRM spatial resolution imaging without changing the optical imaging system or reducing spectrum detection sensitivity
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