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.

中文翻译：

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基于可见光谱图像的古壁画颜料识别

许多古代壁画都面临不可挽回的损害的威胁，并且迫切需要修复。这项工作为古代壁画的可见光谱图像提供了超像素分割方法和颜料识别方法，以指导绘画的科学修复。用于可见光谱图像的超像素分割方法是通过重新定义可见光谱图像的特征对RGB图像进行SLIC（简单线性迭代聚类）的扩展。它可以提取壁画的轮廓并限制壁画修复中的颜料填充区域。选择44种尺寸变化的常用颜料，以建立可见光谱参考数据库，以进行颜料识别。通过在特定构造的特征空间中搜索数据库以找到最接近的参考样本，可以识别每个超像素中使用的颜料。这可以为壁画修复中的颜料选择提供指导。最后，利用多光谱成像系统对敦煌莫高窟采集的可见光谱图像进行了验证。