In recent years， the single image Super-Resolution （SR） reconstruction methods based on Convolutional Neural Network （CNN） have become mainstream. Under normal circumstances， the deeper network layers of the reconstruction model have， the more features are extracted， and the better reconstruction effect is. However， as the number of network layers increases， the reconstruction model will not only have the vanishing gradient problem， but also significantly increase the number of parameters and increase the difficulty of training. To solve the above problems， a single image SR reconstruction method based on dense Inception was proposed. In the proposed method， the image features were extracted by introducing the Inception-Residual Network （Inception-ResNet） structure， and the simplified dense network was adopted globally. And only the path that each module outputs to the reconstruction layer was constructed， avoiding the increase of computation amount caused by the generation of redundant data. When the magnification was 4， the dataset Set5 was used to test the model performance. The results show that， the Structural SIMilarity （SSIM） of the proposed model is 0.013 6 higher than that of accurate image Super-Resolution using Very Deep convolutional network （VDSR）， and the proposed method has the SSIM 0.002 9 higher and the model parameters 78% smaller than Multi-scale residual Network for Image Super-Resolution （MSRN）. The experimental results show that， under the premise of ensuring the depth and width of the model， the proposed method significantly reduces the number of parameters and the difficulty of training. In the meantime， the proposed method can achieve better Peak Signal-to-Noise Ratio （PSNR） and SSIM than the comparison methods.