Abstract: Digital modeling of 3D bone porous structure based on bone slice images is the technical basis of bone tissue engineering and a research hotspot in the field of biomedical engineering. The quality of bone slice image determines the accuracy of the digital model of bone porous structure. However, problems such as data loss, image damage or too small image size may occur in the process of acquiring bone section images, resulting in only partial slice images, and thus such incomplete image information seriously affects the accuracy of 3D porous bone structure modeling. In order to solve this problem, an improved conditional generative adversarial network for complete reconstruction and repair of partial bone slice images was proposed, that is, on the basis of conditional generative adversarial network, the nested residual dense block is added to the generator, and the polarized self-attention module is added to the discriminator. Morphological function analysis and partial porosity distribution study were performed to evaluate the similarity between the reconstructed images and the real bone porous images. The results show that the network can accurately and stably reconstruct complete bone porous slice images.