Confocal Raman Image Method with Maximum Likelihood Method
CUI Han1, WANG Yun1*, QIU Li-rong1, ZHAO Wei-qian1, ZHU Ke2
1. Beijing Key Lab for Precision Optoelectronic Measurement Instrument and Technology, School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, China
2. Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China
Abstract：With the increasing interest in nano microscopic area, such as DNA sequencing, micro structure detection of molecular nano devices, a higher requirement for the spatial resolution of Raman spectroscopy is demanded. However, because of the weak Raman signal, the pinhole size of confocal Raman microscopy is usually a few hundreds microns to ensure a relatively higher spectrum throughput, but the large pinhole size limits the improvements of spatial resolution of confoal Raman spectroscopy. As a result, the convential confocal Raman spectroscopy has been unable to meet the needs of science development. Therefore, a confocal Raman image method with Maximum Likelihood image restoration algorithm based on the convential confocal Raman microscope is propose. This method combines super-resolution image restoration technology and confocal Raman microscopy to realize super-resolution imaging, by using Maximum Likelihood image restoration algorithm based on Poisson-Markov model to conduct image restoration processing on the Raman image, and the high frequency information of the image is recovered, and then the spatial resolution of Raman image is improved and the super-resolution image is realized. Simulation analyses and experimental results indicate that the proposed confocal Raman image method with Maximum Likelihood image restoration algorithm can improve the spatial resolution to 200 nm without losing any Raman spectral signal under the same condition with convential confocal Raman microscopy, moreover it has strong noise suppression capability. In conclusion, the method can provide a new approach for material science, life sciences, biomedicine and other frontiers areas. This method is an effective confocal Raman image method with high spatial resolution.
Key words：Confocal Raman; Maximum Likelihood; Super-resolution; Image restoration
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