Lung CT super-resolution reconstruction based on Laplace residuals

In order to obtain clearer CT images at low doses, this paper proposes a super-resolution reconstruction method of lung CT images based on Laplacian pyramid residual network. The SRResNet network is connected in parallel to solve the problem that the traditional network model adopts a single scale. At the same time, the BN layer in the SRResNet residual module is deleted, and the feature information between the residual blocks is deeply fused through the dense series connection between the residual blocks. Enhance the network's perception of image features. The experimental results show that the lung CT image reconstructed by the algorithm proposed in this paper has richer details and clearer edges.