Author of the publication

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Kernel Bi-Linear Modeling for Reconstructing Data on Manifolds: The Dynamic-MRI Case.

G. Shetty, K. Slavakis, U. Nakarmi, G. Scutari, and L. Ying. EUSIPCO, page 1482-1486. IEEE, (2020)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

No persons found for author name Nakarmi, Ukash

add a person with the name Nakarmi, Ukash

Other publications of authors with the same name

Multi-scale Unrolled Deep Learning Framework for Accelerated Magnetic Resonance Imaging.U. Nakarmi, J. Cheng, E. Rios, M. Mardani, J. Pauly, L. Ying, and S. Vasanawala. ISBI, page 1056-1059. IEEE, (2020)Diagnostic Image Quality Assessment and Classification in Medical Imaging: Opportunities and Challenges.J. Ma, U. Nakarmi, C. Kin, C. Sandino, J. Cheng, A. Syed, P. Wei, J. Pauly, and S. Vasanawala. ISBI, page 337-340. IEEE, (2020)DeepLIR: Attention-Based Approach for Mask-Based Lensless Image Reconstruction.A. Poudel, and U. Nakarmi. WACV (Workshops), page 431-439. IEEE, (2024)Dynamic magnetic resonance imaging using compressed sensing with self-learned nonlinear dictionary (NL-D).U. Nakarmi, Y. Wang, J. Lyu, and L. Ying. ISBI, page 331-334. IEEE, (2015)Kernel and Manifold Framework for Magnetic Resonance Imaging.U. Nakarmi. University at Buffalo, New York, USA, (2018)base-search.net (ftunivbuffalo:oai:ubir.buffalo.edu:10477/77992).Bi-Linear Modeling of Data Manifolds for Dynamic-MRI Recovery.G. Shetty, K. Slavakis, A. Bose, U. Nakarmi, G. Scutari, and L. Ying. IEEE Trans. Med. Imaging, 39 (3): 688-702 (2020)Parallel MRI Reconstruction Using Broad Learning System.Y. Chang, and U. Nakarmi. EMBC, page 2704-2707. IEEE, (2021)On Training Model Bias of Deep Learning based Super-resolution Frameworks for Magnetic Resonance Imaging.M. Shrestha, N. Wang, and U. Nakarmi. BHI, page 1-5. IEEE, (2023)Bi-Linear modeling of manifold-data geometry for Dynamic-MRI recovery.K. Slavakis, G. Shetty, A. Bose, U. Nakarmi, and L. Ying. CAMSAP, page 1-5. IEEE, (2017)Accelerating dynamic magnetic resonance imaging by nonlinear sparse coding.U. Nakarmi, Y. Zhou, J. Lyu, K. Slavakis, and L. Ying. ISBI, page 510-513. IEEE, (2016)

BibSonomy

Disambiguation of "Nakarmi, Ukash"

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Kernel Bi-Linear Modeling for Reconstructing Data on Manifolds: The Dynamic-MRI Case.

Please choose a person to relate this publication to

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Nakarmi, Ukash"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Kernel Bi-Linear Modeling for Reconstructing Data on Manifolds: The Dynamic-MRI Case.

Please choose a person to relate this publication to

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Kernel Bi-Linear Modeling for Reconstructing Data on Manifolds: The Dynamic-MRI Case.