Nonlocal low-rank and sparse matrix decomposition for spectral CT reconstruction

DOI10.1088/1361-6420/aa942czbMath1427.94024OpenAlexW2765757936WikidataQ57193149 ScholiaQ57193149MaRDI QIDQ4607817

Gaohang Yu, Jing Wang, Jianhua Ma, Shanzhou Niu

Publication date: 14 March 2018

Published in: Inverse Problems (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc6168215

zbMATH Keywords

image reconstructionspectral CTnonlocal low-rank and sparse matrix decomposition (NLSMD)photon counting detector (PCD)

Mathematics Subject Classification ID

Computational methods for sparse matrices (65F50) Biomedical imaging and signal processing (92C55) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Numerical linear algebra (65F99)

Related Items (4)

Spatial-spectral cube matching frame for spectral CT reconstruction ⋮ Joint reconstruction in low dose multi-energy CT ⋮ Preface to special issue on joint reconstruction and multi-modality/multi-spectral imaging ⋮ Total image constrained diffusion tensor for spectral computed tomography reconstruction

Cites Work

• An algorithm for total variation minimization and applications
• Robust principal component analysis?
• A Singular Value Thresholding Algorithm for Matrix Completion
• Multi-energy CT based on a prior rank, intensity and sparsity model (PRISM)
• Tensor-Based Formulation and Nuclear Norm Regularization for Multienergy Computed Tomography
• Structure Tensor Total Variation

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