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 highresolution 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.INDEX TERMS Exemplar-based inpainting technique, priority calculation, patch matching, graph cut, multi-resolution information.

Patch size adaptive image inpainting

“…As the gradient is easily interfered by noise and weak texture structure, the ( ) D p value may be inaccurate, it leads to a big difference of ( ) P p , which causes a change in filling order and repair quality. Multi-resolution decomposition is used for image inpainting [34][35][36][37][38][39], noise interference can be reduced and the quality of the image inpainting can be improved. Inspired by multi-resolution image repair, the data items are calculated by multi-resolution images to avoid noise interference.…”

An Adaptive Image Inpainting Method Based on the Weighted Mean

“…To solve the image inpainting problem, there are some intensive approaches [12,13,14] such as partial-differential-equation-based (PDE-based) methods [1,8,15], calculus-of-variation-based methods [8], graph-based methods, stochastic methods, etc. and machine-learning-based methods.…”