Aiming at the problems of poor low-light image quality, noise, and blurred texture, a low-light enhancement network (DF-DFANet) based on dual-frequency domain feature aggregation is proposed. Firstly, a spectral illumination estimation module (FDIEM) is constructed to realize cross-domain feature extraction, which can adjust the frequency domain feature map to suppress noise signals through conjugate symmetric constraints and improve the multi-scale fusion efficiency by layer-by-layer fusion to expand the range of the feature map. Secondly, the multispectral dual attention module (MSAM) is designed to focus on the local frequency characteristics of the image, and pay attention to the detailed information of the image through the wavelet domain space and channel attention mechanism. Finally, the dual-domain feature aggregation module (DDFAM) is proposed to fuse the feature information of the Fourier domain and the wavelet domain, and use the activation function to calculate the adaptive adjustment weight to achieve pixel-level image enhancement and combine the Fourier domain global information to improve the fusion effect. The experimental results show that the PSNR of the proposed network on the LOL dataset reaches 24.3714 and the SSIM reaches 0.8937. Compared with the comparison network, the proposed network enhancement effect is more natural.