Aiming at the problems of blurred image, large noise, and poor visual perception in the traditional image super-resolution reconstruction methods, a method of image super-resolution reconstruction based on hybrid deep convolutional network was proposed. Firstly, the low-resolution image was scaled down to the specified size in the up-sampling phase. Secondly, the initial features of the low-resolution image were extracted in the feature extraction phase. Thirdly, the extracted initial features were sent to the convolutional coding and decoding structure for image feature denoising. Finally, high-dimensional feature extraction and computation were performed on the reconstruction layer using the dilated convolution in order to reconstruct the high-resolution image, and the residual learning was used to quickly optimize the network in order to reduce the noise and make the reconstructed image have better definition and visual effect. Based on the Set14 dataset and scale of 4x, the proposed method was compared with Bicubic interpolation (Bicubic), Anchored neighborhood regression (A+), Super-Resolution Convolutional Neural Network (SRCNN), Very Deep Super-Resolution network (VDSR), Restoration Encoder-Decoder Network (REDNet). In the super-resolution experiments, compared with the above methods, the proposed method has the Peak Signal-to-Noise Ratio (PSNR) increased by 2.73 dB,1.41 dB,1.24 dB,0.72 dB and 1.15 dB respectively, and the Structural SIMilarity (SSIM) improved by 0.067 3,0.020 9,0.019 7,0.002 6 and 0.004 6 respectively. The experimental results show that the hybrid deep convolutional network can effectively perform super-resolution reconstruction of the image.