Aiming at the problem that the inter-layer resolution of computed tomography (CT) sequence images is much lower than the intra-layer resolution, an inter-layer interpolation network for CT images combined with feature pyramid and deformable separated convolution is proposed. The network consists of two modules, the image generation module and the image enhancement module. The image generation module utilizes the MultiResUNet to achieve feature extraction of the input image, and uses two different sets of deformable separation convolutions to generate candidate inter-layer images by performing convolution operations on the input image respectively. The image enhancement module fuses the multi-scale features of the input image through the feature pyramid and the image synthesis network, and generates additional images focusing on contextual details to further enhance the texture details of the candidate inter-layer images. The experimental results show that the inter-layer images generated by the proposed inter-layer interpolation network achieve better results in both qualitative and quantitative analysis, and perform better in the processing of image edge contours and texture details, which can effectively improve the inter-layer resolution of CT sequence images.