Patch-Sparsity-Based Image Inpainting Through a Facet Deduced Directional Derivative

This paper presents a patch-sparsity-based image inpainting algorithm through a facet deduced directional derivative. The algorithm could ensure the continuity of boundaries of the inpainted region and achieve a better performance on restoring the missing structure of an image. In this paper, two improvements are proposed. First, the facet model is introduced to get direction features of the image, which could efficiently reduce the effect of noises. The first-order directional derivatives, along with pixel values, are used to measure the difference between patches. Consequently, a more reliable and accurate matching result is promised. At the same time, the local patch consistency constraint of sparse representation of the target patch is also rewritten in the form of the first-order directional derivative. Therefore, a more precise sparse linear combination could be obtained under constraints for both color and derivative information. Second, the value of patch confidence in the traditional exemplar-based inpainting algorithms drops sharply in the late stage so that the data term or structure sparsity has little influence on priority function. Aiming at this problem, the algorithm makes a modification to the calculating of priority. Thus, the filling order decided by priority function appears more reasonable as a result of a better balance between the values of modified confidence and structure sparsity. Experiments on different types of damages to images show the superiority of the algorithm.