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.