Convergent Complex Quasi-Newton Proximal Methods for Gradient-Driven Denoisers in Compressed Sensing MRI Reconstruction.
- DOI
- 10.1109/tci.2025.3625052
- Published
- 2025
- Container
- IEEE transactions on computational imaging
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1109/tci.2025.3625052,
title = {Convergent Complex Quasi-Newton Proximal Methods for Gradient-Driven Denoisers in Compressed Sensing MRI Reconstruction.},
author = {Hong T and Xu Z and Chun SY and Hernandez-Garcia L and Fessler JA},
year = {2025},
journal = {IEEE transactions on computational imaging},
doi = {10.1109/tci.2025.3625052},
url = {https://doi.org/10.1109/tci.2025.3625052}
}RIS
TY - JOUR TI - Convergent Complex Quasi-Newton Proximal Methods for Gradient-Driven Denoisers in Compressed Sensing MRI Reconstruction. AU - Hong T AU - Xu Z AU - Chun SY AU - Hernandez-Garcia L AU - Fessler JA PY - 2025 JO - IEEE transactions on computational imaging DO - 10.1109/tci.2025.3625052 UR - https://doi.org/10.1109/tci.2025.3625052 ER -
APA
T, H., Z, X., SY, C., L, H., & JA, F. (2025). Convergent Complex Quasi-Newton Proximal Methods for Gradient-Driven Denoisers in Compressed Sensing MRI Reconstruction.. IEEE transactions on computational imaging. https://doi.org/10.1109/tci.2025.3625052
Source records
- pubmed · retrieved 2026-09-25T18:32:53.286Z
- europe-pmc · retrieved 2026-09-25T18:32:53.274Z