Structure guided texture inpainting through multi-scale patches and global optimization for image completion

Automatic image completion can provide convenient editing of consumer images. Most image completion methods find the best patch locally and then copy it to the hole region for texture inpainting. Since the best patch is fixed in size, it is hard to adapt these methods efficiently either to various patterns or to content synthesis. Meanwhile, salient image structures can be estimated and propagated to guide the texture inpainting process for more plausible results. This paper presents a novel image completion method using structure guided texture inpainting. We do not require any interactions to achieve automatic two-stage image completion. In the structure completion stage, the salient structures around the hole region are detected, and then the missing structure curves are completed with Euler spiral. The textures along the structure curves are completed through belief propagation. In the texture inpainting stage, we propose multi-scale patches and global optimization to inpaint the remaining texture in the hole regions guided by the completed structures. First, with defined patch sizes, the hole region is divided into lattice patches, making it possible for multiple patch sizes to render multiscale descriptions of this image. A multi-scale graph is then built for the hole region and formulated as a posterior probability model. Second, using a simulated annealing based Markov chain Monte Carlo method, an inference algorithm is designed to find a global optimization solution for the posterior probability model. The experiments show that our method can automatically complete the hole region and preserve plausible structure shapes of existing ones in various scenarios. The texture inpainting results are more convincing with guidance from the completed structures, and our method can guarantee and accelerate convergence of the global optimization.