Reparameterization Lightweight Residual Network for Super-Resolution of Brain MR Images.

As the demand for high-resolution medical images increases, super-resolution (SR) technology becomes particularly important. In recent years, SR technology based on deep learning has achieved remarkable achievements, and its application in medical images is also growing. Since brain MRI is prone to artifacts during long-term scanning, SR technology has become an effective means to improve image clarity. However, traditional SR methods are usually computationally complex and time-consuming, making them unsuitable for real-time applications. To solve this problem, this paper proposes a lightweight SR model with BSRN as the backbone network and combined with structural re-parameterization to achieve lightweight and efficient SR. The model uses a multi-branch structure during training and integrates the multiple branches into a 3×3 convolution during inference, effectively retaining important feature information. At the same time, the computational complexity and storage requirements are significantly reduced. Through experimental verification on the IXI dataset, this method shows excellent super-resolution reconstruction effects, especially when processing noisy and blurred images, and can effectively improve image clarity and details. Research results show that this method improves model performance and has good application potential, providing new ideas for future medical image processing technology development.