%0 Journal Article
%A CAO Zhi-yi
%A NIU Shao-zhang
%A ZHANG Ji-wei
%T Masked Image Inpainting Algorithm Based on Generative Adversarial Nets
%D 2018
%R 10.13190/j.jbupt.2017-247
%J Journal of Beijing University of Posts and Telecommunications
%P 81-86
%V 41
%N 3
%X A masked image inpainting algorithm based on generative adversarial nets was proposed, which can restore the original image from the lacking of a large number of pixels. Unlike other block search restoration algorithms, the algorithm proposed directly generates possible missing elements and restore them. Due to the improved structure of generated model and the calculation method of generating loss on generative adversarial nets, this article has the characteristics of semi-supervised learning. Experiments show that the proposed method outperforms the existing one on the premise of satisfying the overall contour of the image.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2017-247