全部 |
  • 全部
  • 题名
  • 作者
  • 机构
  • 关键词
  • NSTL主题词
  • 摘要
检索 二次检索 AI检索
外文文献 中文文献
筛选条件:

1. Sparsifying Transform Learning Based Magnetic Resonance Fingerprinting Reconstruction Method NSTL国家科技图书文献中心

Yang Liu |  Min Li... -  《Sixteenth International Conference on Digital Image Processing (ICDIP 2024)》 -  International Conference on Digital Image Processing - 2024, - 共9页

摘要: dictionary matching. Sparsifying transform learning | . Therefore, a sparsifying transform learning based magnetic |  sparsifying transform learning model for the first time |  adaptive sparsity levels got by sparsifying transform |  adaptively learns the transform domain based on image
关键词: Magnetic resonance fingerprinting (MRF) |  Blind compress sensing |  Sparsifying transform learning |  Singular value decomposition (SVD)

2. Group Membership Verification via Nonlinear Sparsifying Transform Learning NSTL国家科技图书文献中心

Behrooz Razeghi |  Marzieh Gheisari... -  《IEEE Access》 - 2024,12 - 86739~86751 - 共13页

摘要:) generating candidate nonlinear transform representations |  representations for both group assignment and transform | In today’s digitally interconnected landscape | , confirming the genuine associations between entities | —whether they are items, devices, or individuals—and
关键词: Transforms |  Servers |  Analytical models |  Data models |  Sparse approximation |  Protocols |  Formal verification

3. A High-Frequency Re-Optimization Network for MRI Reconstruction with CT as the Prior NSTL国家科技图书文献中心

Wenlei Shang |  Wenjian Liu... -  《2024 IEEE International Symposium on Biomedical Imaging》 -  IEEE International Symposium on Biomedical Imaging - 2024, - 1~5 - 共5页

摘要:. Leveraging a sparsifying transform commonly employed in MRI | Prior-constrained deep learning (DL)-based |  reconstruction can reconstruct MR images with high quality | . However, such methods face the inherent risk of |  introducing hallucinations to the reconstructed images
关键词: Deep learning |  Image resolution |  Computed tomography |  Magnetic resonance imaging |  Transforms |  Robustness |  Sensors

4. Multi‐layer clustering‐based residual sparsifying transform for low‐dose CT image reconstruction NSTL国家科技图书文献中心

Ling Chen |  Xikai Yang... -  《Medical Physics》 - 2023,50(10) - 6096~6117 - 共22页

摘要: sparsifying transform (ST) models incur low computational |  clustering‐based residual sparsifying transform (MCST |  sparsifying transform (MARS) prior and PWLS with a union of |  reveal great potential for learning features in | ‐structured ST learning approach for X‐ray computed
关键词: low‐dose CT |  sparsifying transform learning |  statistical image reconstruction

5. Improving accelerated MRI by deep learning with?sparsified complex data NSTL国家科技图书文献中心

Zhaoyang Jin |  Qing‐San Xiang -  《Magnetic resonance in medicine: official journal of the Society of Magnetic Resonance in Medicine》 - 2023,89(5) - 1825~1838 - 共14页 - 被引量:1

摘要: difference transform along the phase‐encoding direction was | Purpose To obtain high‐quality accelerated MR |  images with complex‐valued reconstruction from |  undersampled k‐space data. Methods The MRI scans from human |  subjects were retrospectively undersampled with a regular
关键词: complex convolution |  complex difference transform |  deep learning |  fast imaging |  sparsifying transform

6. MRI reconstruction via multi transforms learning and logarithm ratio for vectorized groups NSTL国家科技图书文献中心

Jianxin Cao |  Shujun Liu -  《Applied Soft Computing》 - 2023,148 - 110861-1~110861-19 - 共19页 - 被引量:1

摘要: learning transform is expected to provide a sparser |  error, in this work, a multi group transforms learning |  sparsifying process with the utilization of nonlocal self |  and global transform, thus resulting in obvious | Compressed sensing (CS) has been demonstrated
关键词: Compressed sensing |  MRI reconstruction |  Multi group transforms learning |  Non-convex logarithm ratio |  Proximal mapping

7. Convolutional Analysis Operator Learning by End-to-End Training of Iterative Neural Networks NSTL国家科技图书文献中心

Andreas Kofler |  Christian Wald... -  《2022 IEEE 19th International Symposium on Biomedical Imaging: IEEE 19th International Symposium on Biomedical Imaging (ISBI), 28-31 March 2022, Kolkata, India》 -  IEEE International Symposium on Biomedical Imaging - 2022, - 1~5 - 共5页

摘要:. Typically, sparsifying transforms are either pre-trained |  the reconstruction. Thereby, learning algorithms are |  the desired properties of the transform. However |  demonstrate how convolutional sparsifying filters can be | The concept of sparsity has been extensively
关键词: Training |  Magnetic resonance imaging |  Neural networks |  Transforms |  Reconstruction algorithms |  Filtering algorithms |  Sensors
NSTL主题词: end-to-end |  Neural network |  Iterative |  Learning

8. A cascade of preconditioned conjugate gradient networks for accelerated magnetic resonance imaging NSTL国家科技图书文献中心

Kim M. |  Chung W. -  《Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice》 - 2022,225 - 107090~107090 - 共6页 - 被引量:2

摘要: training of the sparsifying transform. The optimal |  problem using a deep learning approach. Method: In order | ? 2022 Elsevier B.V.Background and objective | : Recent unfolding based compressed sensing magnetic |  resonance imaging (CS-MRI) methods only reinterpret
关键词: Compressed sensing |  Deep learning |  Magnetic resonance imaging |  Primal-dual

9. Digital inline holographic reconstruction with learned sparsifying transform SCIE Web of Science核心 SCOPUS Scopus数据库 EI 工程索引 NSTL国家科技图书文献中心

Yuan, Shuai |  Cui, Hanchen... -  《Optics Communications: A Journal Devoted to the Rapid Publication of Short Contributions in the Field of Optics and Interaction of Light with Matter》 - 2021,498 - 127220-1~127220-6 - 共6页 - 被引量:3

摘要: reconstruction method based on learned sparsifying transform |  regularization based on a sparsifying transform learned from | We propose a digital inline holographic | . An iterative algorithm which includes the steps of |  updating background, phase, and image, as well as a step
关键词: Lensless inline holographic microscope |  Sparsifying transform learning |  Model-based image reconstruction

10. Motivating Bilevel Approaches To Filter Learning: A Case Study NSTL国家科技图书文献中心

Caroline Crockett |  Jeffrey A. Fessler -  《2021 IEEE International Conference on Image Processing: IEEE International Conference on Image Processing (ICIP), 19-22 Sept. 2021, Anchorage, AK, USA》 -  IEEE International Conference on Image Processing - 2021, - 2803~2807 - 共5页

摘要: transform learning approach and, to learn the transform |  inverse problems is to replace handcrafted sparsifying |  machine learning techniques often improves image |  the learning methodology. This paper compares two |  supervised learning methods. First, the paper considers a
关键词: Training |  Inverse problems |  Supervised learning |  Noise reduction |  Training data |  Transforms |  Machine learning
NSTL主题词: Case studies |  Filters |  Learning
检索条件Sparsifying transform learning
  • 检索词扩展

NSTL主题词

  • NSTL学科导航