MATLAB PROJECT
Channel Splitting
Network for Single MR Image Super-Resolution
Abstract:
High resolution magnetic resonance (MR)
imaging is desirable in many clinical applications due to its contribution to
more accurate subsequent analyses and early clinical diagnoses. Single image
super-resolution (SISR) is an effective and cost efficient alternative
technique to improve the spatial resolution of MR images. In the past few
years, SISR methods based on deep learning techniques, especially convolutional
neural networks (CNNs), have achieved the state-of-the-art performance on
natural images. However, the information is gradually weakened and training
becomes increasingly difficult as the network deepens. The problem is more
serious for medical images because lacking high quality and effective training
samples makes deep models prone to underfitting or overfitting. Nevertheless,
many current models treat the hierarchical features on different channels
equivalently, which is not helpful for the models to deal with the hierarchical
features discriminatively and targetedly. To this end, we present a novel
channel splitting network (CSN) to ease the representational burden of deep
models. The proposed CSN model divides the hierarchical features into two
branches, i.e., residual branch and dense branch, with different information
transmissions. The residual branch is able to promote feature reuse, while the
dense branch is beneficial to the exploration of new features. Besides, we also
adopt the merge-and-run mapping to facilitate information integration between
different branches. The extensive experiments on various MR images, including
proton density (PD), T1, and T2 images, show that the proposed CSN model
achieves superior performance over other state-of-the-art SISR methods.
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