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Adaptive-Order Regression-Based MR Image Super-Resolution

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 506)

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.

Keywords

MRI Regression-based Super-resolution Adaptive order Structure tensor 

Notes

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant 61602065, Sichuan province Key Technology Research and Development project under Grant 2017RZ0013, Scientific Research Foundation of the Education Department of Sichuan Province under Grant No.17ZA0062, J201608 supported by Chengdu University of Information and Technology (CUIT) Foundation for Leaders of Disciplines in Science, project 762001009 supported by Open Fund of CUIT, and project KYTZ201610 supported by the Scientific Research Foundation of CUIT.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Jing Hu
    • 1
  1. 1.Department of Computer ScienceChengdu University of Information TechnologyChengduPeople’s Republic of China

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