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- W2890778977 abstract "To overcome the resolution limitation of magnetic resonance imaging (MRI), performing super-resolution (SR) on these clinical images is needed. Recently, a high-order regression-based SR framework demonstrates its advantage in producing fine details for MR image. However, the high time cost limits its application. To reduce time complexity, an adaptive-order strategy is proposed in this paper. Image structure tensor is used to classify the whole voxels into different groups. Afterward, the regression order is adaptively selected according to the classification result. Numerical experiments demonstrated that the proposed SR method can get a good balance between reconstruction quality and computation efficiency." @default.
- W2890778977 created "2018-09-27" @default.
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- W2890778977 date "2018-09-19" @default.
- W2890778977 modified "2023-09-25" @default.
- W2890778977 title "Adaptive-Order Regression-Based MR Image Super-Resolution" @default.
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- W2890778977 doi "https://doi.org/10.1007/978-3-319-91659-0_19" @default.
- W2890778977 hasPublicationYear "2018" @default.
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