任澳,孔玲君,杨晟炜.基于APSO-LSSVM模型的光谱反射率重建算法研究[J].包装工程,2021,42(11):247-255.
REN Ao,KONG Ling-jun,YANG Sheng-wei.Spectral Reflectance Reconstruction Algorithm Based on APSO-LSSVM Model[J].Packaging Engineering,2021,42(11):247-255. |
| 基于APSO-LSSVM模型的光谱反射率重建算法研究 |
| Spectral Reflectance Reconstruction Algorithm Based on APSO-LSSVM Model |
| 投稿时间：2020-09-25 |
| DOI：10.19554/j.cnki.1001-3563.2021.11.036 |
| 中文关键词: 光谱反射率重建 自适应粒子群算法 最小二乘支持向量机 多光谱图像复现 |
| 英文关键词: spectral reflectance reconstruction adaptive particle swarm optimization least squares support vector machines multispectral image reproduction |
| 基金项目:上海理工大学科技发展项目(2018KJFZ023);绿色制版与柔印标准化实验室资助项目(LGPSFP-03) |
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| 中文摘要: |
| 目的 研究光谱反射率重建算法，提高光谱反射率重建精度。方法 首先通过多光谱相机和分光光度计分别获取Munsell色卡和SG140色卡的通道信息和光谱反射率值，经归一化后将Munsell色卡的通道信息和光谱反射率值作为训练样本的输入和输出。然后，采用APSO算法对LSSVM的最优正则化参数γ和核参数σ进行寻优，构建基于APSO-LSSVM的光谱反射率重构模型。在对模型参数进行寻优过程中，为保持粒子的活性，在粒子群算法中引入自适应惯性权重，并根据遗传算法中的变异思想，加入了变异操作，在普通粒子中引入变异因子。在每次迭代更新中，粒子以一定概率初始化，使粒子群算法可以跳出局部最优解，在较大的空间内进行优化。结果 基于APSO-LSSVM模型对SG140色卡进行光谱反射率重建实验，文中方法的平均色差为0.4677 ，平均均方根误差为0.0006。相较于最小二乘支持向量模型和反向传播神经网络模型的重构精度均有很大的提高。从显色效果来看，文中方法的显色结果更接近真实颜色，人眼基本上难以察觉到两者间的差异。结论 基于APSO-LSSVM的光谱反射率重建算法可以有效地提高光谱反射率重建精度，实现了利用多光谱相机拍摄的多通道信息重构获得精确的多光谱图像。 |
| 英文摘要: |
| The paper aims to study the spectral reflectance reconstruction algorithm and improve the accuracy of spectral reflectance reconstruction. Firstly, the channel information and spectral reflectance of Munsell color card and SG140 color card were obtained by multi-spectral camera and spectrophotometer respectively. After normalization, the channel information and spectral reflectance value of Munsell color card were used as the input and output of training samples. Then, the APSO algorithm was used to optimize the optimal regularization parameters and nuclear parameters of LSSVM, and the spectral reflectance reconstruction model based on APSO-LSSVM was constructed. During the process of the model parameters optimization, in order to maintain the activity of the particle, adaptive inertia weights were introduced into the particle swarm algorithm, and mutation operations were added according to the mutation ideas in genetic algorithms, and some mutation factors were introduced into ordinary particles. In each iteration of the update, the particles were initialized with a certain probability, so that the particle swarm algorithm could jump out of the local optimal solution and optimize in a larger space. Finally,, the average color difference of the method was 0.4677 based on the APSO-LSSVM model to perform spectral reflectance reconstruction experiments on the SG140 color card, and the average root mean square error was 0.0006. Compared with the LSSVM model and the BP neural network model, the reconstruction accuracy was greatly improved. From the perspective of color rendering effect, the color rendering result of the method in this paper was closer to the real color, and the difference between the two was hardly noticeable to the human eye. The spectral reflectance reconstruction model based on APSO-LSSVM could effectively improve the spectral reflectance reconstruction accuracy, and realize the reconstruction of multi-channel information captured by multispectral camera to obtain accurate multispectral images. |
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