LAUSR

Deep Low-rank plus Sparse Network for Dynamic MR Imaging

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1 Published in 2021 at "Medical image analysis"

DOI: 10.1016/j.media.2021.102190

Abstract: In dynamic magnetic resonance (MR) imaging, low-rank plus sparse (L+S) decomposition, or robust principal component analysis (PCA), has achieved stunning performance. However, the selection of the parameters of L+S is empirical, and the acceleration rate… read more here.

Keywords: plus sparse; network; low rank; rank plus ... See more keywords

Low-Tubal-Rank Plus Sparse Tensor Recovery With Prior Subspace Information

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0 Published in 2021 at "IEEE Transactions on Pattern Analysis and Machine Intelligence"

DOI: 10.1109/tpami.2020.2986773

Abstract: Tensor principal component pursuit (TPCP) is a powerful approach in the tensor robust principal component analysis (TRPCA), where the goal is to decompose a data tensor to a low-tubal-rank part plus a sparse residual. TPCP… read more here.

Keywords: tubal rank; plus sparse; prior subspace; tensor ... See more keywords

Iteratively Reweighted Minimax-Concave Penalty Minimization for Accurate Low-rank Plus Sparse Matrix Decomposition.

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0 Published in 2021 at "IEEE transactions on pattern analysis and machine intelligence"

DOI: 10.1109/tpami.2021.3122259

Abstract: Low-rank plus sparse matrix decomposition (LSD) is an important problem in computer vision and machine learning. It has been solved using convex relaxations of the matrix rank and l0-pseudo-norm, which are the nuclear norm and… read more here.

Keywords: low rank; matrix decomposition; rank; plus sparse ... See more keywords