新疆师范大学学报（自然科学版）2024，Vol.43Issue(2)：32-42,11.
基于CartoonGan的改进卡通化图片生成方法
Improved Cartoon Image Generation Method based on GartoonGan
摘要
Abstract
This article proposes an improved cartoon image generation network model aimed at enhancing cartoon effects while preserving semantic information as much as possible.Firstly,a saliency network was designed and in order to prevent overfitting problems and extract further features,residual structures were added to the saliency network to form a residual saliency network and concatenated onto CartoonGan to preserve semantic information;Secondly,adding a cbam attention mechanism to the former further improves the cartoonization effect;Finally,in order to prevent training instability and gradient vanishing during the training process,the least squares loss is used to replace the cross entropy loss,and the significance loss is introduced to constrain the training of the significance network.The experiment showed that through testing on cartoon datasets of Miyazaki Hayao and Makoto Shinkai,the use of FID testing indicators showed some optimization on both datasets.关键词
Cbam注意力机制/显著性网络/残差结构/CartoonGanKey words
Cbam attention mechanism/Saliency network/Residual structure/CartoonGan分类
信息技术与安全科学引用本文复制引用
张文天,于瓅..基于CartoonGan的改进卡通化图片生成方法[J].新疆师范大学学报（自然科学版）,2024,43(2):32-42,11.基金项目
2021年安徽省重点研究与开发计划项目(202104d07020010). （202104d07020010）
