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• The network can directly learn end-to-end mapping between low-resolution images and high-resolution images, and it can use convolution and deconvolution cascade coding to eliminate noise. • Apply dilated convolution layer in the parts, so that small convolution kernel can obtain a large receptive field, which can improve the reconstruction effect and reduce the calculation amount. • Use residual learning method to quickly optimize the network. By adding multiple connections to the network, speed up the training speed and improve the performance. The existing image processing methods had aimed at the problems of blurred image reconstruction, large noise, and poor visual perception. The improved image super-resolution algorithm based on mixed deep convolutional networks is proposed in the paper. Firstly, the proposed method can shrink the low-resolution image to the specified size in upsampling phase. Secondly, it can extract features from low-resolution images. It sends the extracted initial features into the convolutional coding and decoding structure for image features. Thirdly, the feature extraction and calculation in high-dimensions are performed using dilated convolution in reconstruction layer. The high-resolution image had been reconstructed. The proposed method had been compared with state-of-arts on Set5, Set14, BSD100, and Urban100 datasets. The experimental results can show that the Peak Signal-to-Noise Ratio is increased by some ranges, and the Structural Similarity is increased by some effective percentage points. [Display omitted] [ABSTRACT FROM AUTHOR]