A Survey on Improved GAN based Image Inpainting for Different Aims

Abstract

Computer Vision (CV) is an important field of Artificial Intelligence (AI) that focuses on processing and analyzing the visual data input like images or videos. And image inpainting technique is a task of reconstructing image with missing parts by using the information of the existing part, which becomes a crucial part in CV. It has various image processing applications such as digital cultural heritage preservation, old photograph restoration and object removal. The researchers found that using generative adversarial network can extract the feature from image without huge amount of computation which are two main challenges of image inpainting, therefore GAN-based inpainting method has recently been a hub for research. In this paper, we review most of the latest improved image inpainting methods and divide them into three categories according to their different aims: for semantic image inpainting, for generating higher diversity and for irregular or free-form image input. Firstly, we review the methods from structure and algorithm. Then we analyze the advantages and disadvantages of each method. Finally, we conclude the challenges or limitations raised from our findings and some potential development trend.