目的 针对红外与可见光图像在融合过程中，融合图像失真以及可见光图像信息融合不足的问题，提出一种联合多网络结构的红外与可见光图像融合算法。方法 首先采用基于密集残差连接的编码器对输入的红外与可见光图像进行特征提取，然后利用融合策略对得到的特征图进行融合，最后将融合后的特征图送入基于GAN网络的解码器中。结果 通过与可见光图像对抗优化训练，使得融合后的图像保留了更多可见光图像的细节、背景信息，增强了图像的视觉效果。结论 实验表明，与现有的融合算法相比，该算法达到了更好的实验效果，在主观感知和客观评价上都具有更好的表现力。

The work aims to propose a multi-network joint infrared and visible image fusion algorithm, for the purpose of solving the problem of fusion image distortion and insufficient fusion of visible light image information in the fusion process of infrared and visible light images. Firstly, the inputted infrared and visible images were extracted by the encoder based on dense residual connection. Then, the fusion strategy was used to fuse the obtained feature map. Finally, the fused feature map was sent to the decoder based on GAN network. By optimizing the adversarial training with the visible light image, the fused image retained more details and background information of the visible image, and enhanced the visual effect of the image. Experiments show that, compared with the existing fusion algorithm, the proposed algorithm achieves better experimental results and has better expressiveness in subjective perception and objective evaluation.