Aiming at the problems that the current common U-Net network structure and existing image denoising algorithms remove image noise, the processed image is blurry and the edge texture of the image is too smooth and lacks authenticity. An improved U-Net network structure denoising algorithm was proposed. It consists of a denoising module and a multi-feature fusion edge information extraction module. First, the jump connection in U-Net++ is applied to the original U-shaped denoising subnet. Densely connected U-shaped denoising network can reduce the encoder semantic gap between the feature map and the decoder feature map restores a clearer image. Secondly, the edge information extraction module based on the VGG-16 network structure performs feature extraction on the image processed by the denoising network. The U-shaped denoising module is reversely optimized to restore a more realistic image at the same time. Experiments show that testing the algorithm proposed in our article on the common Set5, Set14, Kodak, McMaster data sets is better than the current representative denoising algorithms in the objective evaluation of images. The edge details and texture features of the image are clearer and more realistic, and the visual effect is better.