GP-GAN: Towards Realistic High-Resolution Image Blending | |
Wu, Huikai2,3; Zheng, Shuai1; Zhang, Junge2,3; Huang, Kaiqi2,3 | |
2019-10 | |
会议日期 | 21-25 October 2019 |
会议地点 | Nice, France |
页码 | 2487–2495 |
英文摘要 | 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. The source code is available in \urlhttps://github.com/wuhuikai/GP-GAN, and there's also an online demo in \urlhttp://wuhuikai.me/DeepJS. |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/38526] ![]() |
专题 | 智能系统与工程 |
通讯作者 | Huang, Kaiqi |
作者单位 | 1.University of Oxford 2.University of Chinese Academy of Sciences 3.Institute of Automation, Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Wu, Huikai,Zheng, Shuai,Zhang, Junge,et al. GP-GAN: Towards Realistic High-Resolution Image Blending[C]. 见:. Nice, France. 21-25 October 2019. |
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