Abstract: Computed Tomography (CT) technology plays a crucial role in medical diagnosis. Reducing the radiation dose per projection angle while maintaining a constant number of projection angles is an effective approach to achieving low-dose CT. However, this reduction often introduces significant noise into the reconstructed CT images, adversely affecting subsequent image analysis and research. To address this issue, we propose the Enhanced Restormer for Low-Dose CT Image Reconstruction Based on Multi-Attention Fusion (ERestormer) for low-dose CT image denoising. The network integrates channel attention, receptive field attention, and multi-head transposed attention to enhance the model’s ability to focus on critical information, thereby improving its feature learning capacity. Furthermore, a feature fusion mechanism is introduced to strengthen feature reuse between the encoder and decoder. Experimental results show that the proposed network achieves superior denoising performance and enhanced preservation of image detail when compared to five classical networks: DNCNN, RED-CNN, UNet, Uformer, and Restormer.