DLMRI-Lab: Dictionary Learning MRI Software. DLMRI is a formulation and an algorithm that adaptively learn a dictionary from undersampled k-space measurements and simultaneously reconstruct the MR image (this is an instance of so-called “blind compressed sensing”), as described in the following “DLMRI Paper”: [1] S. Ravishankar and Y. Bresler, “MR image reconstruction from highly undersampled k-space data by dictionary learning,” IEEE Trans. Med. Imag., vol. 30, no. 5, pp. 1028–1041, 2011.

References in zbMATH (referenced in 22 articles )

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  1. Cai, Jian-Feng; Choi, Jae Kyu; Li, Jingyang; Wei, Ke: Image restoration: structured low rank matrix framework for piecewise smooth functions and beyond (2022)
  2. Hou, Ruizhi; Li, Fang: IDPCNN: iterative denoising and projecting CNN for MRI reconstruction (2022)
  3. Cai, Jian-Feng; Choi, Jae Kyu; Wei, Ke: Data driven tight frame for compressed sensing MRI reconstruction via off-the-grid regularization (2020)
  4. Hosseini, A. R.; Esfahani, E. E.: A four directions variational method for solving image processing problems (2020)
  5. Tran, K. H.; Ngolè Mboula, F. M.; Starck, J. L.; Prost, V.: Semisupervised dictionary learning with graph regularized and active points (2020)
  6. Yan, Mengyuan; Duan, Yuping: Nonlocal elastica model for sparse reconstruction (2020)
  7. Guo, Di; Tu, Zhangren; Wang, Jiechao; Xiao, Min; Du, Xiaofeng; Qu, Xiaobo: Salt and pepper noise removal with multi-class dictionary learning and L(_0) norm regularizations (2019)
  8. Huang, Jianping; Wang, Lihui; Zhu, Yuemin: Compressed sensing MRI reconstruction with multiple sparsity constraints on radial sampling (2019)
  9. Romano, Yaniv; Elad, Michael; Milanfar, Peyman: The little engine that could: regularization by denoising (RED) (2017)
  10. Soltani, Sara; Andersen, Martin S.; Hansen, Per Christian: Tomographic image reconstruction using training images (2017)
  11. Wen, Bihan; Ravishankar, Saiprasad; Bresler, Yoram: FRIST-flipping and rotation invariant sparsifying transform learning and applications (2017)
  12. Boyer, Claire; Chauffert, Nicolas; Ciuciu, Philippe; Kahn, Jonas; Weiss, Pierre: On the generation of sampling schemes for magnetic resonance imaging (2016)
  13. Ehrhardt, Matthias J.; Betcke, Marta M.: Multicontrast MRI reconstruction with structure-guided total variation (2016)
  14. Eksioglu, Ender M.: Decoupled algorithm for MRI reconstruction using nonlocal block matching model: BM3D-MRI (2016)
  15. Giryes, Raja: Sampling in the analysis transform domain (2016)
  16. Han, Yu; Du, Huiqian; Lam, Fan; Mei, Wenbo; Fang, Liping: Image reconstruction using analysis model prior (2016)
  17. Li, Yan-Ran; Chan, Raymond H.; Shen, Lixin; Hsu, Yung-Chin; Tseng, Wen-Yih Isaac: An adaptive directional Haar framelet-based reconstruction algorithm for parallel magnetic resonance imaging (2016)
  18. Bi, Dongjie; Xie, Yongle; Zheng, Yahong Rosa: Synthetic aperture radar imaging using basis selection compressed sensing (2015) ioport
  19. Liu, Jianbo; Wang, Shanshan; Peng, Xi; Liang, Dong: Undersampled MR image reconstruction with data-driven tight frame (2015)
  20. Ravishankar, Saiprasad; Bresler, Yoram: Efficient blind compressed sensing using sparsifying transforms with convergence guarantees and application to magnetic resonance imaging (2015)

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