Abstract: Aiming at the problem of fuzzy image generated by generative adversarial networks with random noise as input， difficult convergence in training， and the similarity and diversity of data features cannot be fused by traditional physical data expansion methods， this paper proposes a generative adversarial network-UG-DCGAN based on feature pyramid and attention mechanism CBAM. The method first takes the masked and denoised CT image as input to enhance the robustness of the network. Then use the feature fusion pyramid and attention mechanism to jointly establish a generative network to extract and reconstruct CT images， in which the feature fusion pyramid only retains the maximum scale fusion， and the residual structure is added in the down-sampling process to improve the feature extraction ability. Finally， the convolution layer of the discriminator network is added to improve its supervised judgment ability. After experimental verification， the results compared with the StyleGAN2 model， the FID value of the CT image generated by the algorithm is reduced by 21.98%， and the IS value is increased by 12.44%. It shows that the algorithm has obvious effects on improving the clarity of CT image generation， the similarity and diversity of features.

Key words:  , deep learning； feature pyramid； GAN； image generation； CT