Low-dose computed tomography (CT) imaging reduces radiation dose, but the associated artifacts and noise can compromise diagnostic accuracy. This study proposes a method for denoising low-dose CT images, based on a convolutional neural network and the dual-tree complex wavelet transform (LDTNet). The method exploits the robust information extraction capabilities of convolutional neural networks in the spatial domain and integrates the multi-scale decomposition characteristics of the dual-tree complex wavelet transform in the frequency domain to mitigate information loss typically encountered in conventional single-domain denoising approaches. In addition, the method features a large receptive field, thereby facilitating the capture of subtle structures and edge information. Consequently, the denoising process is further refined. Validation on the AAPM dataset demonstrates that LDTNet achieves significant improvements in both peak signal-to-noise ratio and structural similarity metrics, while visual assessments further confirm its superior performance in noise suppression and image detail restoration.