目的 针对常见分区域图像修复算法中，对于待修复目标的分离效果不佳而导致的修复效果较差等问题，提出一种基于改进K均值聚类的图像修复方案。方法 首先将待分割图像转换到CIELab颜色空间，对a，b分量进行聚类运算，得到K个聚类中心，通过改变聚类迭代次数，得到粗分割结果;然后采用数学形态学对分割结果进行细化处理，精确分离得到目标对象和背景;最后，采用Reinhard算法对目标和背景分别进行色彩迁移，得到图像修复结果。结果 所提模型中的区域分割算法，其分离效果均优于经典的分水岭算法、最大类间方差法和基于Lab通道的最大类间方差算法，采用Reinhard色彩迁移算法图像修复结果比较接近理想修复效果。结论 由最终结果可知，提出修复法的整体效果较为理想，且优于传统的分区域图像修复算法，可为生产实践提供必要的理论依据。

The paper aims to propose an image restoration scheme based on improved K-means clustering to solve the problem of poor restoration effect caused by poor separation effect of the target to be repaired in the common subregional image restoration algorithms. Firstly, the image to be segmented was transformed into CIELab color space, and K clustering centers were obtained by clustering a and b components. The rough segmentation results were obtained by changing the number of clustering iterations. Then, the segmentation results were refined by mathematical morphology, and the target object and background were separated accurately. Finally, the Reinhard algorithm was used to migrate the color of the target and background respectively, and the image restoration results were obtained. The region segmentation algorithm in the model proposed in the article had better separation effects than the classic watershed algorithm, the maximum between-class variance method and the largest between-class variance algorithm based on the Lab channel. The image restoration result using the Reinhard color migration algorithm was closer to the ideal restoration effect. From the final results, it can be concluded that the overall effect of the proposed restoration method is better than that of the traditional subregional image restoration algorithm. It can provide the necessary theoretical basis for production practice.