To improve the accuracy and practicality of the intelligent color-matching application of wood dyeing technology, Fraxinus mandshurica veneer was selected as the dyeing material. First, based on the Friele model and Stearns–Noechel model, the model parameters were cyclically assigned to calculate the optimal fixed parameter values and predictions. Then, particle swarm algorithm was used to optimize two algorithm models, the obtained reflectance curve was fit, and the color differences were calculated according to the human eye-based CIEDE2000 color difference evaluation standard formula. Last, the two formulas to predict the color difference and spectral reflectance were compared. First, the two optimization algorithms were compared according to the size of the fitted color difference value, and then, the most accurate optimization algorithm was selected. When the model parameters were fixed, the average fitted color difference was 0.8202. After optimizing the Friele model, the average fitted color difference was 0.7287, and after optimizing the Stearns–Noechel model, the average fitted color difference was 0.6482. It was concluded that the improved Stearns–Noechel model based on particle swarm method was more accurate than the Friele model for wood color matching.

中文翻译：

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基于粒子群优化的高光谱木材染色色谱算法

为提高木材染色技术智能配色应用的准确性和实用性，选用白蜡木皮作为染色材料。首先，基于Friele模型和Stearns-Noechel模型，循环分配模型参数以计算最优固定参数值和预测值。然后利用粒子群算法对两种算法模型进行优化，拟合得到的反射率曲线，根据基于人眼的CIEDE2000色差评价标准公式计算色差。最后，比较了两种预测色差和光谱反射率的公式。首先根据拟合色差值的大小比较两种优化算法，然后，选择了最准确的优化算法。当模型参数固定时，平均拟合色差为0.8202。Friele模型优化后的平均拟合色差为0.7287，Stearns-Noechel模型优化后的平均拟合色差为0.6482。得出的结论是，基于粒子群方法的改进 Stearns-Noechel 模型在木材颜色匹配方面比 Friele 模型更准确。