A Convergent Generalized Krylov Subspace Method for Compressed Sensing MRI Reconstruction With Gradient-Driven Denoisers

Tao Hong, Umberto Villa, Jeffrey A. Fessler

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DOI: 10.1109/tci.2026.3655489
Published: 2026
Container: IEEE Transactions on Computational Imaging
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1109/tci.2026.3655489,
  title = {A Convergent Generalized Krylov Subspace Method for Compressed Sensing MRI Reconstruction With Gradient-Driven Denoisers},
  author = {Tao Hong and Umberto Villa and Jeffrey A. Fessler},
  year = {2026},
  journal = {IEEE Transactions on Computational Imaging},
  doi = {10.1109/tci.2026.3655489},
  url = {https://doi.org/10.1109/tci.2026.3655489}
}

RIS

TY  - JOUR
TI  - A Convergent Generalized Krylov Subspace Method for Compressed Sensing MRI Reconstruction With Gradient-Driven Denoisers
AU  - Tao Hong
AU  - Umberto Villa
AU  - Jeffrey A. Fessler
PY  - 2026
JO  - IEEE Transactions on Computational Imaging
DO  - 10.1109/tci.2026.3655489
UR  - https://doi.org/10.1109/tci.2026.3655489
ER  -

APA

Hong, T., Villa, U., & Fessler, J. A. (2026). A Convergent Generalized Krylov Subspace Method for Compressed Sensing MRI Reconstruction With Gradient-Driven Denoisers. IEEE Transactions on Computational Imaging. https://doi.org/10.1109/tci.2026.3655489

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crossref · retrieved 2026-11-10T12:17:59.106Z