Structural Knowledge-Guided Feature Inference Network for Image Inpainting

Image inpainting is an essential task in image restoration field. Currently, most meth- ods for image inpainting employ the encoder- decoder framework to restore degraded areas, and this often results in synthesizing wrong se- mantic structure due to the lack of guiding from effective prior information. In this paper, we pro- pose a structural knowledge-guided framework for image inpainting, which predicts both the edge map and corrupted content at the same time. Our model captures structural knowledge in the structure estimation branch to guide the content inference in the latent feature space. By employing self-attention mechanism to aggre- gate known information and inferred structural knowledge, our model is able to synthesize more semantically reasonable content for the corrupted areas. Extensive experiments on three bench- mark datasets demonstrate that our method out- performs most state-of-the-art methods for image inpainting in terms of the evaluation of both vi- sual quality and quantitative metrics.