The existing deep learning networks for MR super-resolution image reconstruction using standard 3D convolutional neural networks typically require a huge amount of parameters and thus excessive computational complexity. This has restricted the development of deeper neural networks for better performance. Here we propose a lightweight separable 3D convolution neural network for MR image super-resolution. Results show that our method can not only greatly reduce the amount of parameters and computational complexity but also improve the performance of image super-resolution.
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