Research on Edge-Guided Image Repair Algorithm

Abstract

The continuous development of deep learning technology has provided new ideas for image repair research over the years, and the image repair methods can understand the semantic information of image through the study of massive image data. Although the existing image repair methods have been able to generate desirable repair results, it is insufficient to deal with the details of missing part from the image when facing the image with more complex missing part, thus the restoration results are excessively smooth or blurry, and the complex structural information that misses from the image cannot be repaired well. In order to solve the issues above, an edge-guided image repair method based on generative adversarial networks technology and the corresponding algorithm are proposed in this paper, and the repair process is divided into two stages. First, the edge repair model is trained to generate more realistic edge information of the missing area. Then, the content generation model is trained to fill in the missing content information based on the edge information that has been repaired. Lastly, the experimental verification is conducted on the CelebA dataset and ParisStreet-View dataset to compare with the Shift-Net model,deep image prior (DIP) model and field factorization machine (FFM) model, and the visual qualitative analysis and quantitative index analysis are carried out on the experimental repair results. The experimental results prove that the repair method proposed in this paper for the missing complex structure information in the image is superior to the existing methods, and also reflect the edge information plays a crucial role in image repair.

Keywords

• |deep learning|image repair|generative adversarial networks (gan)|edge-guided