3D Confocal Raman Spectroscopic High-Resolution imaging method based on RDCAN deep learning model

QI Yakun; XU Demin; CUI Han; QIU Lirong; ZHAO Weiqian

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3D Confocal Raman Spectroscopic High-Resolution imaging method based on RDCAN deep learning model

Optical Sensing and Measurement | 更新时间：2025-10-09

- 3D Confocal Raman Spectroscopic High-Resolution imaging method based on RDCAN deep learning model
- OPTICAL TECHNIQUE Vol. 51, Issue 4, Pages: 491-496(2025)
- 作者机构：
  
  北京理工大学光电学院 “复杂环境智能感测技术”工信部重点实验室，北京 100081
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP394.1;TH691.9
- Received：13 March 2025，
  
  Revised：2025-05-17，
  
  Published：20 July 2025
- 稿件说明：
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齐亚昆,徐德民,崔晗等.基于RDCAN深度学习的三维共焦拉曼光谱高分辨成像方法[J].光学技术,2025,51(04):491-496.

QI Yakun,XU Demin,CUI Han,et al.3D Confocal Raman Spectroscopic High-Resolution imaging method based on RDCAN deep learning model[J].OPTICAL TECHNIQUE,2025,51(04):491-496.
齐亚昆,徐德民,崔晗等.基于RDCAN深度学习的三维共焦拉曼光谱高分辨成像方法[J].光学技术,2025,51(04):491-496. DOI：

QI Yakun,XU Demin,CUI Han,et al.3D Confocal Raman Spectroscopic High-Resolution imaging method based on RDCAN deep learning model[J].OPTICAL TECHNIQUE,2025,51(04):491-496. DOI：

摘要

三维共焦拉曼光谱成像凭借独特的分子特异性和非破坏性原位分析能力，在材料、生物医学等领域得到了广泛应用。然而，其几何形貌分辨率不足的固有缺陷制约了更深层次的应用拓展。因此，提出一种基于残差密集通道注意力网络（RDCAN）的超分辨率图谱成像方法，实现三维共焦拉曼光谱图像到几何形貌的跨模态重建。首先设计端到端的深度学习网络，以残差密集网络为基础，充分融合局部与全局多尺度特征，并引入通道注意力机制动态优化特征权重分配，增强网络的特征提取能力；其次，利用自主搭建的激光差动共焦-拉曼光谱系统采集数据，其中共焦拉曼模块采集的三维光谱数据作为网络输入，差动共焦模块同步获取的几何形貌数据作为真值进行训练，最终实现几何形貌的重建。实验结果表明，重建结果的峰值信噪比达到27.4 dB，结构相似度达到0.98，与差动共焦显微镜测量结果接近。与传统方法相比，方法无需额外硬件即可在传统三维共焦拉曼光谱成像基础上扩展高分辨几何成像能力，实现光谱-几何形貌同源融合，为解决其在几何形貌分辨率上的局限性问题提供了创新解决方案。

Abstract

Three-dimensional confocal Raman spectroscopic imaging is widely used in materials science and biomedicine due to its unique molecular selectivity and non-destructive

in-situ analytical capabilities. However

its inherent limitation of inadequate geometric resolution hinders the development of more advanced applications. In this study

a super-resolution mapping method that employs a Residual Dense Channel Attention Network （RDCAN） to transform 3D confocal Raman spectral images into geometric topography is proposed. An end-to-end deep learning model is designed based on a Residual Dense Network （RDN）

which effectively integrates both local and global multi-scale features. The model incorporates a channel attention mechanism to dynamically optimize feature weight allocation

thereby enhancing feature extraction capability. A self-developed laser differential confocal Raman spectroscopy system is used for data acquisition. In this system

3D spectral data obtained by the confocal Raman module serve as input to the network

while the geometric topography data captured by the differential confocal module serve as ground truth for training. The reconstruction of geometric topography is ultimately realized. Experimental results show that the reconstructed geometric topography achieves a peak signal-to-noise ratio （PSNR） of 27.4dB and a structural similarity index （SSIM） of 0.98

closely matching the measurements obtained by differential confocal microscopy. Compared with traditional methods

the proposed approach enhances high-resolution geometric imaging based on conventional 3D confocal Raman spectroscopy without requiring additional hardware. It also enables spectral-geometric homologous fusion

offering an innovative solution to overcome the limitations in geometric resolution.

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references

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