Given the relatively small proportion of breast cancer in the overall image, which affects the accuracy of early breast cancer detection, this study proposes a wide residual-depth neural network based on convolution residual blocks to restore the high-resolution features of breast cancer magnetic resonance images. The proposed method adopts the combination of global and local residuals, allowing the top layer of the network to directly receive a substantial amount of low-frequency information. A convolution layer is added in front of each residual block for feature pre-extraction, and the sub-pixel convolution layer is used for up-sampling to complete the reconstruction of the low-resolution image. Experiments on the dataset with 260 samples and comparisons with other methods reveal that the proposed network outperforms bicubic interpolation and other deep learning methods in super-resolution of breast cancer magnetic resonance images.