Multi-option image completion based on semantic matching image

In this paper, we propose a new image completion method based on high-accuracy semantic matching. Existing image completion approaches mainly focus on simple filling, ignoring creativity and accuracy in semantic matching. Our method can complete missing regions with more creative and semantically matching images as well as create a more seamless and consistent completed image.We use global and local features to search matching images for the target image. We then complete the missing region using Poisson blending and blending optimization. Results of experiments on challenging image databases show that the images are greatly improved. Thus, these results demonstrate the superiority of the proposed algorithm over existing image completion approaches.