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Due to the dyeing process, learning samples used for color prediction of pre-colored fiber blends should be re-prepared once the batches of the fiber change. The preparation of the sample is time-consuming and leads to manpower and material waste. The two-constant Kubelka-Munk theory is selected in this article to investigate the feasibility to minimize and optimize the learning samples for the theory since it has the highest prediction accuracy and moderate learning sample size requirement among all the color prediction models. Results show that two samples, namely, a masstone obtained by 100% pre-colored fiber and a tint mixed by 40% pre-colored fiber and 60% white fiber, are enough to determine the absorption and scattering coefficients of a pre-colored fiber. In addition, the optimal sample for the single-constant Kubelka-Munk theory is also explored.

Many ancient wall paintings are confronted with the threat of irreversible damages and in urgent requirement of restoration. This work provides the superpixel segmentation method and pigment identification method for the visible spectral image of ancient wall paintings to guide the scientific restoration of the paintings. The superpixel segmentation method for the visible spectral image is an extension of SLIC (Simple Linear Iterative Clustering) for the RGB image by redefining the feature of the visible spectral image. It can extract the outline of wall paintings and limit the pigment filling area in restoration of wall paintings. 44 kinds of commonly used pigments with size variations are selected to construct a visible spectral reference database for pigment identification. The pigment used in each superpixel is identified by searching the database in a specifically constructed feature space to find the nearest reference sample. This can provide guidance to pigment selection in restoration of wall paintings. At last, the methods are validated using the visible spectral image captured from Mogao Grottoes in Dunhuang by using a multispectral imaging system.

A method based on the error space division of principal component analysis (PCA) is proposed to improve both the spectral and colorimetric reconstruction accuracy in spectral dimensionality reduction. Founded on the error source analysis of PCA from a geometric point of view, an objective function minimizing the within-cluster spectral reconstruction error is established to divide the error space of PCA. PCA is implemented again to each divided cluster to reduce the dimensionality of spectral reflectance. The proposed method, error-space-divided PCA (ESDPCA), is tested using four different spectral datasets. The root mean squared error (RMSE) and CIEDE2000 colour difference are adopted as the spectral and colorimetric evaluation metric respectively. Statistical results indicate that ESDPCA can outperform PCA by at least one principal component (PC) in colorimetric accuracy, while it can outperform PCA by at least two or three PCs in spectral accuracy. Comparisons with other three representative methods (i.e., LabPQR, LabRGB, and XYZLMS) show that ESDPCA outperforms them both in spectral and colorimetric accuracy significantly. In addition, the proposed method is robust for spectral datasets and compatible with few other methods involving PCA. Moreover, the computation complexity of ESPCA has the same order of magnitude as that of PCA. [ABSTRACT FROM AUTHOR]