Xiong Yifan, Wu Guangyuan, Li Xiaozhou, Wang Xin
Faculty of Light Industry, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China.
State Key Laboratory of Bio-based Material and Green Papermaking, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China.
Front Psychol. 2022 Nov 23;13:1051286. doi: 10.3389/fpsyg.2022.1051286. eCollection 2022.
An optimized method based on dynamic partitional clustering was proposed for the recovery of spectral reflectance from camera response values. The proposed method produced dynamic clustering subspaces using a combination of dynamic and static clustering, which determined each testing sample as clustering center to obtain the clustering subspace by competition. The Euclidean distance weighted and polynomial expansion models in the clustering subspace were adaptively applied to improve the accuracy of spectral recovery. The experimental results demonstrated that the proposed method outperformed existing methods in spectral and colorimetric accuracy and presented the effectiveness and robustness of spectral recovery accuracy under different color spaces.
提出了一种基于动态划分聚类的优化方法，用于从相机响应值中恢复光谱反射率。该方法结合动态聚类和静态聚类生成动态聚类子空间，通过竞争将每个测试样本确定为聚类中心以获得聚类子空间。在聚类子空间中自适应应用欧几里得距离加权和多项式展开模型，以提高光谱恢复的精度。实验结果表明，该方法在光谱和色度精度方面优于现有方法，并在不同颜色空间下展现了光谱恢复精度的有效性和稳健性。
