为修复破损纺织品文物图像，在Criminisi算法基础上，提出一种改进的基于K-means颜色分割的纺织品文物图像修复算法。根据纺织品文物图像的特点，将RGB图像转化为Lab颜色模型，采用K-means分类器对a*b*层数据基于颜色进行分割处理，对纹样图案边缘进行标定并缩小匹配块搜索区域；引入L值的标准差来表示颜色离散度，对优先权函数以及自适应匹配块进行改进。用所提算法与文献报道的3种算法对自然破损纺织品文物图像和人为破损纺织品图像进行修复，并对修复结果进行评价。实验结果表明，所提算法修复的图像纹理自然、结构合理，峰值信噪比、结构相似性、特征相似性、均方误差值更好。

For the inpainting of the images of textile cultural relics at the damaged parts, an improved algorithm is pro-posed based on K-means color segmentation and Criminisi algorithm. Due to the characteristics of textile cultural relics images, RGB images were converted into Lab color model, and K-means classifier was used to segment a* and b * layer data according to their colors to calibrate the edges of the patterns and narrow the search area of matching blocks. The standard deviation of L value was introduced to represent the color dispersion and the priority function and adaptive matching block were improved.The proposed algorithm and the three algorithms reported in the literature were used to repair the image of natural damaged textile relics and man-made damaged textile images, and the restoration results were evaluated. The experimental results show that the image restored by the proposed algorithm has natural texture, reasonable structure, and better peak signal-to-noise ratio, structural similarity, feature similarity, mean square error values.