Image Inpainting Based on Structural Tensor Edge Intensity Model

Abstract

In the exemplar-based image inpainting approach, there are usually two major problems: the unreasonable calculation of priority and only considering the color features in the patch lookup strategy. In this paper, we propose an image inpainting approach based on the structural tensor edge intensity model. First, we use the progressive scanning inpainting method to avoid the image filling order being affected by the priority function. Then, we use the edge intensity model to build the patches similarity function for correctly identifying the local image structure. Finally, the balance operator is used to restrict the excessive propagation of structural information to ensure the correct structural reconstruction. The experimental results show that the our approach is comparable and even superior to some state-of-the-art inpainting algorithms.