Image inpainting with a learned guidance vector field

Image inpainting is one of the challenging problems in image restoration. To recover the missing region, we can only rely on the information in the uncorrupted region of the input image and some prior knowledge. The latter can be learned from suitable training data or implemented through some smoothness constraints. In this paper, a new approach for image inpainting is proposed. Here, we iteratively learn a guidance vector field from training data and recover the missing region by solving the Poisson equation using the learned guidance vector field with Dirichlet boundary conditions. In addition, we also propose a method to select the best training set by using the correlation between neighboring patches of the damaged input image and training images. The experimental results on face images show that the new approach yields smooth and visually pleasing results.