Unsupervised GAN Multi-focus Image Fusion Based on Attention Mechanism

Jiang Yunfeng; Li Weitong

doi:10.12052/gdutxb.240097

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Unsupervised GAN Multi-focus Image Fusion Based on Attention Mechanism

更新时间：2026-05-27

- Unsupervised GAN Multi-focus Image Fusion Based on Attention Mechanism
- Vol. 42, Issue 5, Pages: 112-120(2025)
- 作者机构：
  
  广东工业大学信息工程学院,广东,广州,51000
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.240097
  CLC： TP391
- Published：2025
- 稿件说明：
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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.

Jiang Yunfeng, Li Weitong. Unsupervised GAN Multi-focus Image Fusion Based on Attention Mechanism[J]. 2025, 42(5): 112-120.
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. DOI： 10.12052/gdutxb.240097

Jiang Yunfeng, Li Weitong. Unsupervised GAN Multi-focus Image Fusion Based on Attention Mechanism[J]. 2025, 42(5): 112-120. DOI： 10.12052/gdutxb.240097.

摘要

针对现有多聚焦图像融合方法在聚焦和散焦区域出现边界模糊、信息丢失等问题，提出了一个无监督生成对抗网络。通过复合注意力特征提取模块构成生成器，充分提取源图像的全局和局部特征，加强对图像颜色信息的学习；利用源图像的联合梯度作为鉴别器的输入，增强其纹理细节的提取；结合结构相似度和峰值信噪比提出了结构感知损失，进一步提高融合图像质量。Lytro数据集的实验结果表明：与7种代表性的融合算法相比，该方法在主客观评价方面均取得了良好的融合性能，其中指标PSNR、AG、SF、EI达到了52.38、8.25、22.74、85.96，分别比次优算法提高了5.5%、2.2%、1.4%、2.1%。

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

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

Keywords

references

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