针对低采样下重构图像的成像质量和成像速度问题，提出一种基于卷积神经网络的计算鬼成像方法.首先，利用关联计算方法重建的一组训练集图像和相应的无损图像训练一个卷积神经网络；然后，将通过关联计算重建的测试集图像作为卷积神经网络的输入层，使其能够学习传感模型并最终能够预测出相应的图像；最后，将经卷积神经网络还原的图像分别与计算鬼成像和结合压缩感知算法重建的图像进行详细的对比实验分析.实验结果表明：本文方法在0.08采样率下能优质量地还原出被测物体的像，且成像质量均高于其他方法；同时，在不牺牲图像质量的条件下，执行程序所需的单张图像重建耗时约0.06 s，极大地提高了图像重建的速度.分别通过数值模拟和光学实验，验证了本方法的有效性，对工程应用具有重要意义.

A computational ghost imaging method based on convolutional neural network is proposed to solve the imaging quality and the speed of reconstructed images under low sampling condition. Firstly, a convolutional neural network is trained by using a set of training images which are reconstructed by the correlation calculation method and corresponding lossless images. Then, the test set images reconstructed by the correlation calculation are used as the input layer of the convolutional neural network to learn the sensing model and predict the corresponding images. Finally, the images reconstructed by the convolutional neural network are compared with the images reconstructed by computational ghost imaging and compressed sensing algorithm, respectively. The experimental results show that the proposed method can restore the measured object with high quality when the sampling rate is 0.08, and the image quality is higher than other methods. Meanwhile, it takes about 0.06 s without sacrificing image quality when the method is used to reconstruct the single image, which greatly improves the speed of image reconstruction. The effectiveness of our method is also verified by numerical simulation and optical experiments, which is of great significance for engineering applications.