Difference between revisions of "ISMORE"
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| − | <meta name="title" content="SMORE & iSMORE: Single Image Super-Resolution"/> | + | <!-- <meta name="title" content="SMORE & iSMORE: Single Image Super-Resolution"/> --> |
{{h2|Synthetic Multi-Orientation Resolution Enhancement (SMORE)}} | {{h2|Synthetic Multi-Orientation Resolution Enhancement (SMORE)}} | ||
{{TOCright}} | {{TOCright}} | ||
| − | <u>S</u>ynthetic <u>M</u>ulti-<u>O</u>rientation <u>R</u>esolution <u>E</u>nhancement (SMORE) and its iterative variant (iSMORE) are single image super-resolution techniques. Some of the associated | + | <u>S</u>ynthetic <u>M</u>ulti-<u>O</u>rientation <u>R</u>esolution <u>E</u>nhancement (SMORE) and its iterative variant (iSMORE) are single image super-resolution techniques. Some of the associated publications are: |
| − | {{iacl-pub|author=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|title=Applications of a deep learning method for anti-aliasing and super-resolution in MRI|jrnl=mrm|number=64:132-141|when=2019|doi=10.1016/j.mri.2019.05.038|pubmed=31247254}} | + | {{-}} |
| − | {{iacl-pub|author=C. Zhao, S. Son, Y. Kim, and J.L. Prince|title=iSMORE: An Iterative Self Super-Resolution Algorithm|when=Simulation and Synthesis in Medical Imaging (SASHIMI 2019) held in conjunction with the {{iacl-pub miccai2019}}|number=130-139|period=|doi=10.1007/978-3-030-32778-1_14}} | + | {{iacl-pub| author = C Zhao, B.E. Dewey, D.L. Pham, P.A. Calabresi, D.S. Reich, and J.L. Prince| title = SMORE: A Self-supervised Anti-aliasing and Super-resolution Algorithm for MRI Using Deep Learning| jrnl = tmi| number = 40(3):805-817| when = 2021| doi = 10.1109/TMI.2020.3037187}} |
| + | {{iacl-pub| author = 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 | title = Applications of a deep learning method for anti-aliasing and super-resolution in MRI | jrnl = mrm| number = 64:132-141| when = 2019| doi = 10.1016/j.mri.2019.05.038| pubmed = 31247254}} | ||
| + | {{iacl-pub| author = C. Zhao, S. Son, Y. Kim, and J.L. Prince | title = iSMORE: An Iterative Self Super-Resolution Algorithm | when = Simulation and Synthesis in Medical Imaging (SASHIMI 2019) held in conjunction with the {{iacl-pub miccai2019}} | number = pp. 130-139 | period =| doi = 10.1007/978-3-030-32778-1_14}} | ||
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| + | Complete the [https://forms.office.com/r/T4Bmm7DUM2 download form for access]. | ||
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| + | Specifying a single iteration within the shell script is equivalent to running SMORE. If you use the code in anyway please cite some of our papers. | ||
Latest revision as of 00:52, 3 July 2022
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:
- 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)
Complete the download form for access.
Specifying a single iteration within the shell script is equivalent to running SMORE. If you use the code in anyway please cite some of our papers.
