| 基于深度学习的图像风格迁移研究进展 |
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| 引用本文： | 陈淮源,张广驰,陈高,周清峰.基于深度学习的图像风格迁移研究进展[J].计算机工程与应用,2021,57(11):37-45. |
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| 作者姓名： | 陈淮源 张广驰 陈高 周清峰 |
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| 作者单位： | 1.广东工业大学 信息工程学院，广州 510006
2.东莞理工学院 电子工程与智能化学院，广东 东莞 523808 |
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| 摘 要： | 图像风格迁移是计算机视觉领域的一个热点研究方向。随着深度学习的兴起，图像风格迁移领域得到了突破性的发展。为了推进图像风格迁移领域的发展，对基于深度学习的图像风格迁移的现有研究方法进行综述。对基于深度学习的图像风格迁移方法进行分类和梳理，并对比分析基于卷积神经网络和基于生成对抗网络的风格迁移方法，介绍了图像风格迁移的改进性和拓展性工作，讨论了图像风格迁移领域目前面临的挑战和未来的研究方向。
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| 关 键 词： | 图像风格迁移 深度学习 卷积神经网络 生成对抗网络 |
Research Progress of Image Style Transfer Based on Deep Learning |
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| CHEN Huaiyuan,ZHANG Guangchi,CHEN Gao,ZHOU Qingfeng.Research Progress of Image Style Transfer Based on Deep Learning[J].Computer Engineering and Applications,2021,57(11):37-45. |
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| Authors: | CHEN Huaiyuan ZHANG Guangchi CHEN Gao ZHOU Qingfeng |
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| Affiliation: | 1.School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
2.School of Electrical Engineering & Intelligentization, Dongguan University of Technology, Dongguan, Guangdong 523808, China |
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| Abstract: | Image style transfer is one of the hot research directions in the field of computer vision. With the rise of deep learning, the field of image style transfer has made a breakthrough. In order to promote the development of image style transfer, the existing research methods of image style transfer based on deep learning are reviewed. Firstly, the image style transfer methods based on deep learning are classified and combed, and the style transfer methods based on convolutional neural network and generative adversarial network are compared and analyzed. Then, the improvements and expansions of image style transfer are introduced. Finally, the current challenges and future research directions in the field of image style transfer are discussed. |
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| Keywords: | image style transfer deep learning convolutional neural network generative adversarial network |
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