在大口径光学元件表面疵病初检时，灰尘和麻点由于形态类似，不易区分。针对该问题，提出了一种基于模式识别理论的灰尘麻点判别方法。该判别方法以既有的疵病检测系统为基础，根据灰尘麻点的暗场成像特点，选取了合适的特征并根据因子分析理论对特征进行变换，最后基于贝叶斯判别原理对灰尘麻点进行分类。采用自制定标板建立了灰尘麻点训练样本库，并进行多组实验，选取了合适的判别函数，最后进行了对未知样品表面灰尘麻点的区分。实验结果表明，该判别方法的正确率可以达到95%以上。目前此判别方法已经用于惯性约束聚变系统中大口径光学元件表面灰尘与麻点自动区分。

In surface defects evaluation of large fine optics, dusts and digs are very difficult to be distinguished from each other. In this paper, a pattern-recognition-based automatic discrimination method between dusts and digs is proposed to solve the problem. Based on the existing surface defects evaluation system(SDES), the process of feature selection and feature extraction is described on the basis of factor analysis. The dusts and the digs are classified according to the principle of Bayes discrimination. With the images of the calibration board, a data set that can be used for training and identifying dusts and digs is prepared. In order to obtain the suitable classifier, several comparison experiments are performed. The classifier has also been employed to the unlabeled data to verify the theory. The result shows that the accuracy is above 95%, which greatly reduces the number of false alarm that would otherwise result in remanufacturing. This method has already been applied to classifying dusts and digs for large fine optics in the inertial confinement fusion (ICF) system.