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Threshold choices of Huber regularization using global- and local-edge-detecting operators for x-ray computed tomographic reconstruction.

H. Zhang, S. Zhang, D. Hu, D. Zeng, Z. Bian, L. Lu, J. Ma, and J. Huang. EMBC, page 2352-2355. IEEE, (2013)

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Jian Bian

Shan Bian

Xin Bian

Shun Bian

Ning Bian

Other publications of authors with the same name

Helical CT Reconstruction From Sparse-View Data Through Exploiting the 3D Anatomical Structure Sparsity.Y. Wang, G. Chen, T. Xi, Z. Bian, D. Zeng, H. Zaidi, J. He, and J. Ma. IEEE Access, (2021)Direct Energy-resolving CT Imaging via Energy-integrating CT images using a Unified Generative Adversarial Network.L. Yao, S. Li, M. Zhu, D. Zeng, Z. Bian, and J. Ma. CoRR, (2019)Iterative Reconstruction for X-Ray Computed Tomography Using Prior-Image Induced Nonlocal Regularization.H. Zhang, J. Huang, J. Ma, Z. Bian, Q. Feng, H. Lu, Z. Liang, and W. Chen. IEEE Trans. Biomed. Eng., 61 (9): 2367-2378 (2014)Optimizing a Parameterized Plug-and-Play ADMM for Iterative Low-Dose CT Reconstruction.J. He, Y. Yang, Y. Wang, D. Zeng, Z. Bian, H. Zhang, J. Sun, Z. Xu, and J. Ma. IEEE Trans. Medical Imaging, 38 (2): 371-382 (2019)Threshold choices of Huber regularization using global- and local-edge-detecting operators for x-ray computed tomographic reconstruction.H. Zhang, S. Zhang, D. Hu, D. Zeng, Z. Bian, L. Lu, J. Ma, and J. Huang. EMBC, page 2352-2355. IEEE, (2013)Blind CT image quality assessment via deep learning strategy: initial study.S. Li, J. He, Y. Wang, Y. Liao, D. Zeng, Z. Bian, and J. Ma. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, volume 10577 of SPIE Proceedings, page 105771A. SPIE, (2018)Low-Dose Dynamic Cerebral Perfusion Computed Tomography Reconstruction via Kronecker-Basis-Representation Tensor Sparsity Regularization.D. Zeng, Q. Xie, W. Cao, J. Lin, H. Zhang, S. Zhang, J. Huang, Z. Bian, D. Meng, Z. Xu and 3 other author(s). IEEE Trans. Medical Imaging, 36 (12): 2546-2556 (2017)A new Mumford-Shah total variation minimization based model for sparse-view x-ray computed tomography image reconstruction.B. Chen, Z. Bian, X. Zhou, W. Chen, J. Ma, and Z. Liang. Neurocomputing, (2018)Combined global and local information for blind CT image quality assessment via deep learning.Q. Gao, S. Li, M. Zhu, D. Li, Z. Bian, Q. Lv, D. Zeng, and J. Ma. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, volume 11316 of SPIE Proceedings, page 1131615. SPIE, (2020)Noise Characteristics Modeled Unsupervised Network for Robust CT Image Reconstruction.D. Li, Z. Bian, S. Li, J. He, D. Zeng, and J. Ma. IEEE Trans. Medical Imaging, 41 (12): 3849-3861 (2022)

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Disambiguation of "Bian, Zhaoying"

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Threshold choices of Huber regularization using global- and local-edge-detecting operators for x-ray computed tomographic reconstruction.

Please choose a person to relate this publication to

Jian Bian

Shan Bian

Xin Bian

Shun Bian

Ning Bian

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Bian, Zhaoying"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Threshold choices of Huber regularization using global- and local-edge-detecting operators for x-ray computed tomographic reconstruction.

Please choose a person to relate this publication to

Jian Bian

Shan Bian

Xin Bian

Shun Bian

Ning Bian

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
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Threshold choices of Huber regularization using global- and local-edge-detecting operators for x-ray computed tomographic reconstruction.