Improving Image Inpainting based on Structure and Texture Information Using Quadtree

Abstract

In this paper, we present a novel and efficient algorithm for image inpainting based on the structure and texture components. In our method, after decomposing the image into its texture and structure components using PCA (Principal Component Analysis), these components are inpainted separately using the proposed algorithm. Finally, the inpainted image is simply acquired by adding the two inpainted images. For structure inpainting we used quadtree concept to identify the importance of each pixel located on the boundary of the target region. Subsequently, we detect the correct path for filling so that this path demonstrates an orientation for the better structure inpainting. It is noteworthy that structure inpainting is more important because human vision is sensitive to the coherence of structure. For texture inpainting, we use Euclidean distance in the texture component for patch selection. Also, the geometric feature is considered by LSK (Local Steering Kernel) in the original image to assist chooing a better patch candidate. The experimental results of our algorithm demonstrate the effectiveness of the proposed method.