广东工业大学学报2025，Vol.42Issue(5)：112-120,9.DOI:10.12052/gdutxb.240097
基于注意力机制的无监督GAN多聚焦图像融合
Unsupervised GAN Multi-focus Image Fusion Based on Attention Mechanism
摘要
Abstract
An unsupervised generative adversarial network is proposed to solve the problems of boundary blurring and information loss in the focusing and defocusing regions of existing multi-focus image fusion methods.By constructing a generator with complex attention feature extraction module,the global and local features of the source image can be fully extracted and the learning of image color information can be strengthened.The combined gradient of the source image is used as the input of the discriminator to enhance the extraction of texture details.Combined with structural similarity and peak signal-to-noise ratio,the structure perception loss is proposed to further improve the quality of the fused image.The experimental results of Lytro data set show that,compared with 7 representative fusion algorithms,this method achieves good fusion performance in both subjective and objective evaluation,among which the indices PSNR,AG,SF,and EI reach 52.38,8.25,22.74,and 85.96,respectively,representing improvements of 5.5%,2.2%,1.4%,and 2.1%over the second-best algorithm.关键词
多聚焦图像融合/注意力机制/无监督学习/生成对抗网络Key words
multi-focus image fusion/attention mechanism/unsupervised learning/generative adversarial network分类
信息技术与安全科学引用本文复制引用
蒋云峰,李伟彤..基于注意力机制的无监督GAN多聚焦图像融合[J].广东工业大学学报,2025,42(5):112-120,9.基金项目
广东省科技计划项目(2017A010101016) （2017A010101016）
