计算机与现代化Issue(9)：61-66,72,7.DOI:10.3969/j.issn.1006-2475.2025.09.009
基于CycleGAN和注意力机制的人脸素描图像转换
Facial Sketch Image Conversion Based on CycleGAN and Attention Mechanism
摘要
Abstract
In recent years,because of its demand in law enforcement,criminal and entertainment fields,face sketch-photo syn-thesis has become a research hotspot.As deep learning model without paired image supervision,CycleGAN is good at cross-domain image conversion,providing a powerful tool for efficient conversion between sketches and photos.In view of the difficulty of collecting a large number of pairs of face images and sketch images,and the problems of fuzzy and low definition image details in face sketch image generation,an improved CycleGAN model is proposed.In this paper,the self-attention mechanism is intro-duced into the residual block of the ResNet architecture generator in the CycleGAN model,so that the CycleGAN generator model can learn the features of different channels and the importance of different regions in the face image more effectively,and automatically focus on the important regions of facial features,such as eyes,nose,mouth,etc.,during image processing.At the same time,the edge clarity and integrity of the sketch are increased,so as to improve the quality of the generated face sketch im-age.The proposed model is implemented on the datasets CUHK and FS2K.The structural similarity,peak signal-to-noise ratio and multi-scale structural similarity are 0.7741,11.7451 and 0.8504 respectively on CUHK and 0.7049,13.2745 and 0.7970 re-spectively on FS2K.These results outperformed the comparison models of CycleGAN,Pix2Pix,MUNIT,and DCLGAN.Accord-ing to the comparison experiment and subjective vision,the proposed model can effectively complete the process of face sketch-ing and generate higher quality face sketching images.关键词
CycleGAN/生成对抗网络/注意力机制/残差网络Key words
CycleGAN/generative adversarial network/attention mechanism/residual network分类
信息技术与安全科学引用本文复制引用
林睿姿,姚达,戴欣,沈国誉,王嘉慧,万伟国..基于CycleGAN和注意力机制的人脸素描图像转换[J].计算机与现代化,2025,(9):61-66,72,7.基金项目
国家自然科学基金资助项目(62261025) （62261025）
江西省自然科学基金资助项目(20232BAB212015) （20232BAB212015）
