针对多通道拉曼成像系统常会受荧光背景、噪声等非线性因素的影响而导致拉曼光谱重建结果一般的问题，提出了一种基于高斯核主成分分析的拉曼光谱重建算法.首先利用相似度因子对标定样本数据集进行预处理，其次通过高斯核函数将标定样本以非线性形式映射至高维特征空间，接着在特征空间中对映射后的数据集提取基函数并通过伪逆法求得与之对应的基函数系数.使用聚甲基丙烯酸甲酯作为测试样本，并引入均方根误差来评估拉曼光谱重建结果的准确性.实验结果表明，相比传统的伪逆法与维纳估计法，该算法具有更高的重建精度及抗噪能力，且能有效降低标定样本中不良数据和成像系统中非线性因素对拉曼光谱重建的影响.因此，该算法可以为多通道拉曼快速成像提供一种有效的拉曼光谱重建算法.

The multi-channel Raman imaging system is often affected by the nonlinear factors such as fluorescence background and noise, which reduces the Raman spectral reconstruction accuracy. Therefore, a reconstruction algorithm based on Gaussian kernel principal component analysis was proposed, in which the calibration samples are optimized by similarity factor; Then the calibration samples were mapped to high-dimensional space in a nonlinear form by using kernel function; The basis function was extracted from the mapped data set, and the basis function coefficients were obtained by pseudo-inverse method. Polymethyl methacrylate was used in the experiment and the Raman spectral reconstruction accuracy was evaluated in terms of relative root mean square error. The experimental results show that the proposed algorithm has higher reconstruction accuracy and anti-noise property than the traditional pseudo-inverse and wiener estimation methods. And the proposed algorithm can effectively reduce the impact of bad data and nonlinear factors in the calibration samples and imaging system. Therefore, the proposed algorithm can provide an effective Raman spectral reconstruction algorithm for multi-channel Raman imaging.