关键词: 图像补全, 生成式对抗网络, 卷积神经网络, 马尔科夫随机场, 均方误差

Abstract: Image completion is an important research direction in the field of digital image processing and has broad application prospects. This paper proposes an image completion method based on generative adversarial networks (GAN). The generative adversarial networks model consists of two parts: the generator model and the discriminator model, all of which are implemented by convolutional neural network (CNN). Firstly, the missing region of the image is complemented by the generator model. Then, the complemented image is discriminated by the discriminator model. In order to enhance the processing power of image texture details, Markov random field (MRF) and mean square   error (MSE) are used as the loss function to train the generator model. The experimental results show that the image completion method based on the generative adversarial networks has better completion effect than other existing methods.

Key words: image completion, generative adversarial networks (GAN), convolutional neural network (CNN), Markov random field (MRF), mean square error (MSE)