Image Inpainting Method with Generative Adversarial Network Using Gaussian Filter

Abstract

In recent years, researches on image inpainting by deep learning are rapidly progressing. Until now, there have been many studies on partial inpainting of facial images and landscape images with Generative Adversarial Network (GAN). But it was difficult to apply it in real time. In this research, a GAN-based image inpainting method that can reduce learning and processing time is proposed. In this method, GAN is used for the network structure, and the mask is updated using the Gaussian filter in the generator part of GAN. Through verification experiments using actual images, this method is several to 10 times faster than conventional methods such as Partial Convolution and Gated Convolution.