Exemplar-Based Image Inpainting with Multi-Resolution Information and the Graph Cut Technique

Abstract

Filling holes in an image is achieved in a manner similar to peeling the onion. The order of filling affects the image inpainting results, especially concerning the content of complex images. When high-resolution images are used to extract edge information, they are susceptible to high-frequency information, such as complex textures and noise. Furthermore, edge information is extracted in different resolutions, while the main contour information of the image can be obtained more easily. In this paper, multi-resolution information is used to prioritize which target patches in an image to fill, which helps to elucidate the optimal sequence for image repair. Multi-resolution images provide more information than single-resolution images, and similar patches are computed on multi-resolution images to obtain multiple candidate patches. Similar patch calculations use a variety of information on colors, gradients, and boundaries to more accurately search for similar patches. We chose the most reasonable candidate patch by means of the structural similarity index measure (SSIM). When pasting the patch to fill the target region, we used graph cut technology to eliminate blockiness. Compared with the state-of-the-art repair algorithm, the experimental results prove that the proposed repair algorithm can repair the image very well.