Fine-grained Adversarial Image Inpainting with Super Resolution

Abstract

Image inpainting refers to synthesizing plausible contents for images with missing regions. However, current methods often create blurry textures, distorted structures and loss of details, especially when the image has complex scenes or large missing regions. We propose a fine-grained adversarial image inpainting model with super resolution. It performs a coarse-to-fine inpainting procedure in two stages. The proposed generator first synthesizes initial predictions of the missing regions with a novel encoder-decoder structure. Then it refines the predicted missing regions by generating high-frequency details via super resolution. We evaluate the proposed from both pixel level and semantic level. Experiments demonstrate that the proposed can generate higher quality inpainting results than the baseline models in both metrics.