Exemplar-based image inpainting via fast global optimal searching
Youdong Ding, Bing Yu · 2017
This paper presents an image inpainting algorithm which relies on the fast global optimal searching. The whole framework of our method is focused on approximating the minimum of the proposed energy function with additional new texture term. We combine four steps in the framework. First, the initialization is carried out layer by layer in a fast way. Second, we use PatchMatch algorithm to realize the search of approximate nearest neighbors for patches in the missing region. Third, a weighted mean based image reconstruction method is implemented iteratively. Last, we use Poisson editing to handle the illumination of missing region to get realistic looking image. In addition, the iterative calculation is embedded in a multi-resolution pyramid. Experimental results show that our algorithm can effectively maintain the texture and structure characteristics of the missing region.