# 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.

## Key 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.