Aiming at the problem of poor denoising effect and long training period in image denoising， an image denoising model based on approximate U-shaped network structure was proposed. Firstly， the original linear network structure was modified to an approximate U-shaped network structure by using convolutional layers with different strides. Then， the image information of different receptive fields was superimposed on each other to preserve the original information of the image as much as possible. Finally， the deconvolutional network layer was introduced for image restoration and further noise removal. Experimental results show that on Set12 and BSD68 test sets： compared with Denoising Convolutional Neural Network （DnCNN） model， the proposed model has an average increase of 0.04 to 0.14 dB on Peak Signal-to-Noise Ratio （PSNR）， and an average reduction of 41% on training time， verifying that the proposed model has better denoising effect and shorter training time.