拉曼成像是拉曼光谱技术非常重要的一个环节, 通过生成光谱数据的伪彩图像, 可以得到采集区域中某物质组分的浓度和位置分布信息, 当前, 拉曼成像技术已经逐渐成为监测生物活性以及物质组分的最优解之一。 为了得到清晰的成像效果, 采集过程中的数据量不宜过小, 否则成像效果差、 锯齿感较重, 从而导致视觉效果不好。 但是, 数据量的增加虽然可以得到更好的成像效果, 但会增加时间成本、 降低仪器寿命。 因此, 在不增加时间和硬件成本的情况下, 对采集点数据进行插值提高成像的空间分辨率、 降低时间分辨率是很有意义的。 提出了一种基于研究拉曼光谱波形结构物理特性的图像插值算法。 区别于传统的图像插值算法仅对图像像素值进行处理, 通过结合拉曼信号的物理特性, 选取最小二乘法和物理特性上最适合解析拉曼谱峰的数学模型Voigt函数对现有光谱数据进行数学拟合提取特征值, 并对提取出的特征值通过线性插值方法计算出未知插值点的Voigt函数的特征值, 从而计算出插值点的Voigt函数, 可以直接提高现有拉曼图像的空间分辨率, 同时通过该方法也可以缩短扫描时间, 提高拉曼成像的时间分辨率。 同时, 为了验证算法的有效性和可行性, 对一种药物和一种生物细胞的原始拉曼成像进行图像插值, 并采用直方图的欧几里得距离求解相似度和结构相似度算法(SSIM, 一种权威的图像相似度评价算法)对插值效果进行评价。 实验结果表明, 在图像像素点增量分别为50%和75%的情况下, 该算法能够很好地保留样本组分的分布和浓度等重要信息。 该算法可以在不升级硬件的情况下提高拉曼成像的性能, 推荐作为拉曼成像数据处理和软件的有效补充。

Raman imaging is a very important part of Raman spectroscopy technology. By generating pseudo-color images of spectral data, a substance component's concentration and location distribution information in the collected area can be obtained. At present, Raman imaging technology has gradually become one of the optimal solutions for monitoring biological activity and substance components. In order to obtain a clear imaging effect, the amount of data in the collection process should not be too small otherwise, the imaging effect is poor, the serrations are heavy, and the visual effect is not good. However, although the increase in the amount of data can get a better imaging effect, it will increase the time cost and reduce the instrument's life. Therefore, it is of great significance to improve the spatial resolution of the imaging and reduce the temporal resolution of the imaging by interpolating the data of the collection points without increasing the time and hardware cost. In this paper, an image interpolation algorithm based on the study of the physical properties of Raman spectral waveform structure is proposed. Different from traditional image interpolation algorithm of image pixel values only for processing, by combining the Raman signal of physical properties, least square method and physical properties of the most suitable mathematical model of the Raman spectral peaks Voigt function of existing mathematical fitting spectrum data are extracted eigenvalue, and the extracted eigenvalue by linear interpolation method to calculate the unknown interpolation point. The spatial resolution of existing Raman images can be directly improved by calculating the Voigt function of interpolation points based on the eigenvalue of the GT function. Meanwhile, the scanning time can also be shortened, and the temporal resolution of Raman images can be improved by this method. At the same time, in order to verify the effectiveness and feasibility of the proposed algorithm, the original Raman images of a drug and a biological cell were interpolated, and the histogram Euclidean distance algorithm and structural similarity algorithm (SSIM, an authority image similarity evaluation algorithm) were used to evaluate the interpolation effect. The experimental results show that the proposed algorithm can preserve important information such as the distribution and concentration of sample components well when the pixel increment is 50% and 75% respectively. The proposed algorithm can improve the performance of Raman imaging without upgrading the hardware and is recommended as an effective supplement to the data processing and software of Raman imaging.