Computed Tomography (CT) is a critical diagnostic tool, but the growing use of CT has raised concerns about patient radiation exposure. Sparse-view CT, which reduces the quantity of projection angles, has been proposed as a potential solution. However, traditional reconstruction methods, such as Filtered Back Projection (FBP), face challenges in producing high-quality images from sparse data. To address these challenges, the DD-ReconNet model for CT image reconstruction is introduced. This model leverages both the Sinogram and Image domains to enhance image quality and includes three stages: Sinogram Restoration, Image Reconstruction using FBPConvNet, and Image Restoration. The Sinogram and Image Restoration modules integrate the Swin Transformer V2 block and an Improved Edge Convolution layer to boost restoration performance. In addition, a hybrid objective function is employed to optimize the reconstructed images. Experimental results indicate the superiority of the DD-ReconNet model over conventional methods, positioning it as a promising approach for low-dose and sparse-view CT reconstruction, which improves diagnostic accuracy while minimizing radiation exposure.