[1]徐志鹏,卢官明,罗燕晴.基于 CycleGAN 的人脸素描图像生成[J].计算机技术与发展,2021,31(08):63-68.[doi:10. 3969 / j. issn. 1673-629X. 2021. 08. 011]
　XU Zhi-peng,LU Guan-ming,LUO Yan-qing.Face Sketch Image Generation Based on CycleGAN[J].,2021,31(08):63-68.[doi:10. 3969 / j. issn. 1673-629X. 2021. 08. 011]
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基于 CycleGAN 的人脸素描图像生成()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年08期
页码:
63-68
栏目:
图形与图像
出版日期:
2021-08-10

文章信息/Info

Title:
Face Sketch Image Generation Based on CycleGAN
文章编号:
1673-629X(2021)08-0063-06
作者:
徐志鹏; 卢官明; 罗燕晴
南京邮电大学 通信与信息工程学院,江苏 南京 210003
Author(s):
XU Zhi-peng; LU Guan-ming; LUO Yan-qing
School of Telecommunication & Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210003,China
关键词:
CycleGAN; 生成对抗网络; 风格转换; 人脸素描; 注意力机制; 残差模块
Keywords:
CycleGAN; generative adversarial networks; style transfer; face sketch; attention mechanism; residual block
分类号:
TP391. 41
DOI:
10. 3969 / j. issn. 1673-629X. 2021. 08. 011
摘要:
CycleGAN 是一种基于生成对抗网络的衍生模型,可以在缺少成对训练图像的条件下实现两个具有不同风格的图像域之间的相互转换。 由于收集大量成对的人脸图像和素描图像存在较大的难度,并且针对人脸素描图像生成任务中存在的图像细节模糊和低清晰度的问题,提出一种改进的 CycleGAN 模型。 通过引入基于注意力机制的残差模块,让CycleGAN 的生成器模型可以更加有效地学习不同通道特征和人脸图像中不同区域的重要程度,降低人脸图像中无用信息对生成模型的影响,从而提升生成的人脸素描图像的质量。通过对比实验发现,使用基于注意力机制的 CycleGAN 模型生成的素描人脸图像质量较好,且更完整清晰地保留了较丰富的面部特征信息,优于 CycleGAN 和 DualGAN 模型,充分证明了基于注意力机制的改进 CycleGAN 模型的有效性。
Abstract:
CycleGAN is a derivative model based on generative adversarial network,which can realize the mutual conversion between two image domains with different styles in? the absence of paired training images. Because it is difficult to collect a large number of pairs of face images and sketch images,in order to solve the problem of blurred image details and low definition in the task of generating face sketch images,an improved CycleGAN model is proposed. By introducing the residual module based on? the attention mechanism,the CycleGAN generator model can learn the importance of different channel features and different regions in the face image more effectively, reducing the impact of useless information in the face image on the generation model,there by improving the quality of the generated face sketch image. Through comparative experiments,it is found that the sketched face image generated by the CycleGAN model based on the attention mechanism is of better quality, and retains more complete and richer facial feature information, which is better than the CycleGAN and DualGAN models. It is fully proved that the CycleGAN model based on attention mechanism is effective.

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更新日期/Last Update: 2021-08-10