基于CGAN网络的二阶段式艺术字体渲染方法

叶武剑; 高海健; 翁韶伟; 高智; 王善进; 张春玉; 刘怡俊

doi:10.12052/gdutxb.180162

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基于CGAN网络的二阶段式艺术字体渲染方法

综合研究 | 更新时间：2026-05-27

- 基于CGAN网络的二阶段式艺术字体渲染方法
- A Two-stage Effect Rendering Method for Art Font Based on CGAN Network
- 广东工业大学学报 2019年36卷第3期页码：47-55
- 作者机构：
  
  1. 广东工业大学　信息工程学院,广东,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
  3. 西藏民族大学　信息工程学院,陕西,咸阳,712082
- 作者简介：
  
  叶武剑(1987−)，男，讲师，博士，主要研究方向为深度学习、计算机视觉、计算机网络等
  翁韶伟(1980−)，女，副教授，博士，主要研究方向为图像可逆水印、信息隐藏、视频篡改等. E-mail：wswweiwei@126.com
- 基金信息：
  
  国家自然科学基金资助项目(61872095，61872128，61571139，61201393);广东省信息安全技术重点实验室开放课题基金资助项目(2017B030314131);广东省智能信息处理重点实验室、深圳市媒体信息内容安全重点实验室2018年开放基金课题资助项目(ML-2018-03);西藏自治区自然科学基金资助项目(2016ZR-MZ-01);广州市珠江科技新星专题项目(2014J2200085);广东工业大学青年百人项目资助项目(220413548)
- DOI：10.12052/gdutxb.180162
  中图分类号： TP391
- 网络首发：2019-05-09，
  
  纸质出版：2019
- 稿件说明：
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叶武剑, 高海健, 翁韶伟, 高智, 王善进, 张春玉, 刘怡俊. 基于CGAN网络的二阶段式艺术字体渲染方法[J]. 广东工业大学学报, 2019, 36(3): 47-55.

Ye Wu-jian, Gao Hai-jian, Weng Shao-wei, et al. A Two-stage Effect Rendering Method for Art Font Based on CGAN Network[J]. 2019, 36(3): 47-55.
叶武剑, 高海健, 翁韶伟, 高智, 王善进, 张春玉, 刘怡俊. 基于CGAN网络的二阶段式艺术字体渲染方法[J]. 广东工业大学学报, 2019, 36(3): 47-55. DOI： 10.12052/gdutxb.180162.

Ye Wu-jian, Gao Hai-jian, Weng Shao-wei, et al. A Two-stage Effect Rendering Method for Art Font Based on CGAN Network[J]. 2019, 36(3): 47-55. DOI： 10.12052/gdutxb.180162.

摘要

艺术字体渲染是媒体排版的重要技术之一. 如何提供一种高效的艺术字体渲染方法来实现生成艺术字体的特效多样化与清晰化是亟待解决的问题. 本文借助条件生成式对抗网络(Conditional Generative Adversarial Networks

CGAN)

提出一个包括风格化处理和清晰化处理的二阶段式艺术字体渲染方法

对字体实现高效的特定效果渲染. 首先

风格化处理是通过构建风格化网络模型对多样化的字体进行各种不同的2D或3D特效渲染; 然后

构建清晰化网络模型对生成的艺术字体图进行清晰化处理

这克服了单一GAN网络生成图模糊的缺陷. 实验结果表明

二阶段式艺术字体渲染方法所生成的特效字体的纹理细节较为丰富

不受限于文字骨架

而且字体清晰度也得到较大提升; 同时

该方法对字体的特效渲染批量化处理效率也明显提高

具有较强实用价值.

Abstract

Art font rendering is one of the important techniques of media typesetting. How to provide an efficient art font rendering method to realize the diversity and clearness of the special effects of generative art fonts is an urgent problem for technicians in this field. With the help of Conditional Generative Adversarial Networks (CGAN)

a two-stage art font rendering method that includes style transfer processing and enhancement processing is proposed to achieve high quality rendering of art fonts. First

by style transfer processing

a stylized network model is built to render various 2D or 3D special effects on a variety of input fonts. Then

by enhancement processing

a sharpening network model is built to sharpen the generated art font images

which overcomes the defect of fuzzy image generation in a single GAN network. The experimental results show that

comparing with existing methods

this scheme has a better performance for generating more diverse and more clear special art fonts

whose texture details are relatively rich

and are not limited to the text skeleton. At the same time

this scheme can also improve the efficiency of art font rendering in the batch processing and has a higher practical value.

关键词

Keywords

references

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