Improved Semantic Image Inpainting Method with Deep Convolution Generative Adversarial Networks

With the development of generative adversarial networks (GANs), more and more researchers apply them to image inpainting technologies. However, many existing approaches caused some inpainting images to be unclear or even restore failures due to a failure to keep the consistency of the inpainted content and structures in line with the surroundings. In this article, we propose the Improved Semantic Image Inpainting Method with Deep Convolution GANs, which can resolve this inconsistency. In the proposed method, we design a patch discriminator and contextual loss to jointly perform the accuracy and effectiveness for image inpainting. In addition, we also designed a consistency loss based on deep convolutional neural networks to constrain the difference between the generated image and the original image in the feature space. Our proposed method improves the details and authenticity effectively for the inpainting images. We evaluate our proposed method on two different datasets, and the result shows that our proposed method achieves state-of-the-art results.