Nonlinear Poisson Image Completion using Color Manifold

While most image completion methods focus on filling regions with structures or stationary textures, few are suitable for completing large-scale missing parts on complex background with nonlinearly progressive color changes. In this paper, we propose a novel approach, termed as nonlinear Poisson completion, to solve this problem. The visible parts of the background serve as a training set, from which we learn the embedding nonlinear subspace of progressive colors, namely color manifold. A Poisson image completion procedure, which works efficiently for smoothly linear interpolation, is extended to nonlinearly recover the missing regions with iteration solution confined to the manifold. In some especially challenging cases, a simple post-processing serves to generate more natural-looking results. Experiments on both synthetic and real images verify the effectiveness of the proposed algorithm.