Edge-guided generative adversarial network for image inpainting

Abstract

In this paper, we present an edge-guided generative adversarial network (EGGAN) for edge-based image inpainting that can be adopted in image compression and transmission error concealment. Our key idea is to integrate edges into the generative network, and train the generative network to minimize both gradient loss and adversarial loss. Given a corrupted image and the estimated edges of the missing area, the trained generative network is capable in generating the missing area in a visually plausible manner, and meanwhile reproducing the given edges faithfully. Experimental results on the challenging face images have shown the effectiveness of EGGAN.