Aimed at the problems that the VDSR model convolution kernel is single and the DRRN model fails to take advantages of global features, a combined convolution image superresolution model is proposed based on parallel residual convolution neural networks. Firstly, the combined convolution neural layer is structured by the original convolution layer and dilated convolution layer, and the skip connection approach is employed to connect the different layers to take advantage of different level features, completing superresolution network. There are two advantages of this model:①Combination of dilated convolution neural layers and original convolution layers can capture multiscale features without computationconsuming. Based on this approach, the network can get more presentation capacity. ②Skip connection approach fuse lowlevel information and highlevel information. From this approach, different level features can be learned. This means that stronger learning ability can be obtained. Based on the experiment results on multiple data sets, more than 0.1 IFC improvement is achieved, compared with the stateoftheart models VDSR, DRRN, SRCNN in most tasks.