Image denoising, a critical process in computer vision, aims to restore high-quality images from their noisy counterparts. Significant progress in this field has been made possible by the emergence of various effective deep learning models. However, these methods are typically confined to processing within the single-domain and exhibit weak performance in preserving detailed information, hindering their practical application. To address this issue, we propose an efficient Dual-Domain Information Fusion Network (DIFNet) for image denoising. Specifically, we design an aggregate frequency domain and spatial domain network to capture and fuse the detailed information. The DIFNet employs a Dual-Domain Feature Fusion Module (DFFM) to integrate the extracted dual-domain information, facilitating the recalibration of weights between the spatial and frequency domains, thereby emphasizing and restoring detailed information. In the DFFM, frequency domain information is extracted through a Frequency Domain Attention Module (FDAM), while spatial domain information is acquired via the convolutional blocks in the image denoising baseline model NAFNet. Experimental results demonstrate that the dual-domain denoising method can recover more detail while maintaining denoising performance. Furthermore, the proposed method outperforms state-of-the-art approaches on widely used benchmarks, highlighting its superior performance.