MR image deep network super-resolution method based on multiple optimization

The invention relates to an MR image deep network super-resolution method based on multiple optimization, solves the problem that the model complexity and training difficulty are difficult to achieve better balance when a super-resolution MR image is reconstructed by using a deep learning network in the prior art, and belongs to the technical field of image super-resolution reconstruction. The method comprises the following steps: acquiring an MR image training set, wherein the MR image training set comprises a multi-slice low-resolution MR image set and a corresponding multi-slice high-resolution MR image set; using the MR image training set to train a super-resolution deep learning network, wherein the super-resolution deep learning network comprises a fusion layer and a plurality of super-resolution networks, the input of each super-resolution network is a low-resolution MR image, the output of each super-resolution network is simultaneously input to the fusion layer, and the fusion layer outputs a high-re