GP-GAN: Towards Realistic High-Resolution Image Blending

TL;DR

This paper introduces GP-GAN, a novel high-resolution image blending framework combining classical gradient methods with GANs, achieving realistic results with fewer artifacts and outperforming existing methods.

Contribution

The paper presents the first application of GANs to high-resolution image blending, integrating Gaussian-Poisson equations with a new Blending GAN for improved quality.

Findings

Achieves state-of-the-art performance on Transient Attributes dataset.

Produces high-resolution, realistic blended images with fewer artifacts.

User study favors the proposed method over alternatives.

Abstract

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.