WHERE IS THE FAKE? PATCH-WISE SUPERVISED GANS FOR TEXTURE INPAINTING

We tackle the problem of texture inpainting where the input images are textures with missing values along with masks that indicate the zones that should be generated. Many works have been done in image inpainting with the aim to achieve global and local consistency. But these works still suffer from limitations when dealing with textures. In fact, the local information in the image to be completed needs to be used in order to achieve local continuities and visually realistic texture inpainting. For this, we propose a new segmentor discriminator that performs a patch-wise real/fake classification and is supervised by input masks. During training, it aims to locate the generated parts (which we will call fake parts in consistency with the GAN framework), thus making the difference between real and fake patches given one image. We tested our approach on the publicly available DTD dataset, as well as Electo-Magnetic borehole images dataset and showed that it achieves state-of-the-art performances and better deals with local consistency than existing methods.