ISMORE
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Synthetic Multi-Orientation Resolution Enhancement (SMORE)
Synthetic Multi-Orientation Resolution Enhancement (SMORE) and its iterative variant (iSMORE) are single image super-resolution techniques. Some of the associated publications are:
- S.W. Remedios, S. Han, L. Zuo, A. Carass, D.L. Pham, J.L. Prince, and B.E. Dewey, "Self-Supervised Super-Resolution for Slice Gap MRI", Simulation and Synthesis in Medical Imaging (SASHIMI 2023) in conjunction with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023), Vancouver, Canada, October 8–12, 2023.
- C Zhao, B.E. Dewey, D.L. Pham, P.A. Calabresi, D.S. Reich, and J.L. Prince, "SMORE: A Self-supervised Anti-aliasing and Super-resolution Algorithm for MRI Using Deep Learning", IEEE Trans. on Medical Imaging, 40(3):805-817, 2021. (doi)
- C. Zhao, M. Shao, A. Carass, H. Li, B.E. Dewey, L.M. Ellingsen, J. Woo, M.A. Guttman, A.M. Blitz, M. Stone, P.A. Calabresi, H. Halperin, and J.L. Prince, "Applications of a deep learning method for anti-aliasing and super-resolution in MRI", Magnetic Resonance in Medicine, 64:132-141, 2019. (doi) (PubMed)
- C. Zhao, S. Son, Y. Kim, and J.L. Prince, "iSMORE: An Iterative Self Super-Resolution Algorithm", pp. 130-139, Simulation and Synthesis in Medical Imaging (SASHIMI 2019) held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2019), Shenzhen, China, October 13 - 17, 2019. (doi)
SMORE can be downloaded from GitLab.com/iacl/smore
If you use the code in anyway please cite some of our papers.
