针对太赫兹直线加速器，开发了基于EPICS分布式系统的横向截面尺寸测量系统。该系统采用束斑检测器完成束斑到光斑的转换，并通过远心镜头将光斑成像到CCD相机，完成对光斑图像的采集，之后基于ADAravis将相机采集的图像数据汇入到EPICS数据库。由于暗电流以及环境辐射的影响，在采集到的图像中会存在椒盐噪声，因此使用卷积神经网络（CNN）对图像中的椒盐噪声进行抑制，最后对图像进行高斯拟合计算出束流截面尺寸。实验结果表明，CNN可以有效地消除椒盐噪声，并且系统的分辨率达到15.8 μm，满足系统设计要求。

The “Composite Light Source” project of the National Synchrotron Radiation Laboratory, Terahertz Near-Field High-Flux Material Property Testing System, consists of an approximately 3-meter electron linear accelerator. To characterize the performance of the accelerator and monitor the status of the beam, it is necessary to measure the beam size. Specifically designed for the terahertz linear accelerator, a beam size measurement system based on the EPICS distributed system has been developed. A beam spot detector is taken for the conversion of the beam spot into an optical spot and a remote mirror is taken to image the optical spot onto a CCD camera for image acquisition. Subsequently, the camera-captured image data is integrated into the EPICS database using ADAravis. Due to the dark current and radiation environment, salt-and-pepper noise is present in the acquired images. Therefore, a Convolutional Neural Network (CNN) is employed to suppress the salt-and-pepper noise in the images. Finally, Gaussian fitting is applied to calculate the beam cross-sectional dimensions from the images. The experimental results indicate that the CNN can effectively eliminate salt-and-pepper noise, and the resolution of this system is 15.8 μm, which satisfies the design requirement.