Deep convolutional neural networks (CNNs) have attracted much attention in the field of image denoising. However, with the increase in network depth, most deep CNNs have problems such as performance saturation and learning decline. In this paper, a dual-path denoising network combining local and global features is proposed. Two networks with different structures are combined to form a dual path model, and the width of the network is increased to obtain more different features. The global and local features are integrated through long path connections to enhance interlayer correlation. The attention mechanism uses the current stage to guide the input of the previous stage to obtain more features. The experimental results show that the PSNR values of the proposed network model reach 32. 95 dB and 31.74 dB in Set12 and BSD68 datasets, respectively. At the same time, the subjective visual effects such as image edges and other details are recovered better and clearer.