针对复合材料在X射线单一能量下CT图像对比度低、细节信息不清晰等问题，提出一种基于生成对抗网络的双能CT图像融合方法，用于融合高低能量条件下的图像。该网络由一个生成器和两个判别器组成，生成器用于提取CT图像的细节信息，两个判别器用于区分融合CT图像和高低能CT图像之间的结构差异，判断数据的真假。通过端到端模型的对抗训练完成融合模型的构建，最后生成包含高低能信息的融合CT图像。试验结果表明，提出的基于生成对抗网络的双能CT图像融合算法，很好地突破了单能X射线CT成像的局限性，融合后的双能CT图像细节更加丰富，有利于复合材料关键图像信息的判读。

In order to solve the problems of low contrast and unclear detail information of composite CT images under single X-ray energy, a dual-energy CT image fusion method based on generative adversarial network was proposed to fuse images under low and high energy condition. The network consists of a generator and two discriminators. The generator was used to extract the details of CT images. Meanwhile, two discriminators were used to distinguish the structural differences between fusion CT images and high-low energy CT images, and to distinguish the authenticity of data. The fusion model was established through the adversarial training of the end to end model, and finally the fusion CT image containing high and low energy information was generated. The experimental results show that the proposed dual-energy image fusion algorithm based on generative adversarial network solves the limitations of single-energy CT imaging well, and the fused dual-energy CT image has more details, which was beneficial to the interpretation of key image information of composite materials.