  It is common but challenging to address high-resolution image blending in the
automatic photo editing application. In this paper, we would like to focus on
solving the problem of high-resolution image blending, where the composite
images are provided. We propose a framework called Gaussian-Poisson Generative
Adversarial Network (GP-GAN) to leverage the strengths of the classical
gradient-based approach and Generative Adversarial Networks. To the best of our
knowledge, it's the first work that explores the capability of GANs in
high-resolution image blending task. Concretely, we propose Gaussian-Poisson
Equation to formulate the high-resolution image blending problem, which is a
joint optimization constrained by the gradient and color information. Inspired
by the prior works, we obtain gradient information via applying gradient
filters. To generate the color information, we propose a Blending GAN to learn
the mapping between the composite images and the well-blended ones. Compared to
the alternative methods, our approach can deliver high-resolution, realistic
images with fewer bleedings and unpleasant artifacts. Experiments confirm that
our approach achieves the state-of-the-art performance on Transient Attributes
dataset. A user study on Amazon Mechanical Turk finds that the majority of
workers are in favor of the proposed method.
