模式识别与人工智能
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模式识别与人工智能  2020, Vol. 33 Issue (11): 959-971    DOI: 10.16451/j.cnki.issn1003-6059.202011001
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梯度控制与鉴别特征引导的写实类人物肖像插画转换方法
施荣晓1, 叶东毅1, 陈昭炯1
1.福州大学 数学与计算机科学学院 福州 350108
Gradient Controlled and Discriminative Features Guided Image-to-Image Translation Method Towards Realistic Portrait Illustrations
SHI Rongxiao1, YE Dongyi1, CHEN Zhaojiong1
1. College of Mathematics and Computer Science,Fuzhou University,Fuzhou 350108

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摘要 

现有的无监督图像转换方法由于未考虑人脸辨别特征保持这一问题,转换后得到的写实类人物肖像插画常会出现人脸变形和面部结构坍塌的现象,难以辨认人物信息.针对该问题,文中提出梯度控制与鉴别特征引导的写实类人物肖像插画转换方法.在循环生成对抗网络(CycleGAN)的基础上引入避免冗余特征复用的掩码残差长连接,将图像梯度信息一致性作为约束条件,较好地保持人脸辨别特征.设计鉴别特征引导的信息共享训练机制,使生成器具有和鉴别器相同的提取目标风格图像鉴别特征的能力.同时拓展图像块鉴别器为多感知鉴别器,获得丰富的鉴别信息.实验表明,文中方法转换得到的写实类人物肖像插画不仅较好地保持显著的人脸辨别特征,而且在插画视觉效果上较优.

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施荣晓
叶东毅
陈昭炯
关键词 人物肖像插画图像转换循环生成对抗网络(CycleGAN)图像梯度信息一致性鉴别特征    
Abstract

Without considering the preservation of facial recognition characteristics,the existing unsupervised image-to-image translation methods often suffer from face distortion and facial structure collapse.Consequently,it is difficult to identify the personal information after translation.To tackle this issue,gradient controlled and discriminative features guided image-to-image translation method towards realistic portrait illustrations is proposed based on cycle-consistent generative adversarial network(CycleGAN).Masked residual long connections are introduced by the proposed method to avoid reusing redundant features and image gradient information consistency is considered as a constraint to preserve facial recognition characteristics.In addition,a discriminative feature guided information-shared training mechanism is devised and thus generators are capable of capturing discriminative features of target images,like the discriminators.Moreover,patch-level discriminators are extended to multi-awareness discriminators to obtain more discriminative information.Experimental results show that the proposed method preserves facial recognition characteristics well in the translated illustrations and outperforms the existing unsupervised image-to-image translation methods in visual effect of illustrations.

Key wordsPortrait Illustration    Image-to-Image Translation    Cycle-Consistent Generative Adversarial Network(CycleGAN)    Image Gradient Consistency    Discriminative Feature   
收稿日期: 2020-08-13     
ZTFLH: TP391.41  
基金资助:

国家自然科学基金项目(No.61672158)、福建省自然科学基金项目(No.2018J01798)资助

通讯作者: 叶东毅,博士,教授,主要研究方向为计算智能、数据挖掘.E-mail:yiedy@fzu.edu.cn.   
作者简介: 施荣晓,硕士研究生,主要研究方向为智能图像处理.E-mail:690086805@qq.com.陈昭炯,硕士,教授,主要研究方向为智能图像处理、计算智能.E-mail:chenzj@fzu.edu.cn.
引用本文:   
施荣晓, 叶东毅, 陈昭炯. 梯度控制与鉴别特征引导的写实类人物肖像插画转换方法[J]. 模式识别与人工智能, 2020, 33(11): 959-971. SHI Rongxiao, YE Dongyi, CHEN Zhaojiong. Gradient Controlled and Discriminative Features Guided Image-to-Image Translation Method Towards Realistic Portrait Illustrations. , 2020, 33(11): 959-971.
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