The existing image completion methods have the problems of structural distortion on visual connectivity and easy to overfitting in the process of training. In order to solve the problems, a new image completion method of Generative Adversarial Network (GAN) based on two discrimination networks was proposed. One completion network, one global discrimination network and one local discrimination network were used in the completion model of the proposed method. The broken area of image to be completed was filled by a similar patch as input in the completion network, which greatly improved the speed and quality of the generation images. The global marginal structure information and feature information were used comprehensively in the global discrimination network to ensure that the completed image of completion network conformed visual connectivity. While discriminating the output image, the assisted feature patches found from multiple images were used to improve the generalization ability of discrimination in the local discrimination network, which solved the issue that the completion network was easily overfitting with too concentrated features or single feature. The experimental results show that, the proposed completion method has good completion effect on face images, and has good applicability in different kinds of images. The Peak Signal-to-Noise Ratio (PSNR) and Structural SIMilarity (SSIM) of the proposed method are better than those of the state-of-the-art methods based on deep learning.