Many deep learning models for MR image restoration have high computational cost, which raises significant hardware cost and also restricts their usage. To address it, we propose a lightweight network based on the encoder-decoder architecture which integrates image features of different scales and levels to improve the representation capability. A novel loss function is also designed to constrain the model in both image domain and frequency domain. The experimental results show that our model efficiently reduces computational burden while maintaining high performance compared to other conventional models.
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