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Found 4 papers, 3 papers with code
1D Probabilistic Undersampling Pattern Optimization for MR Image Reconstruction
We propose a 1D probabilistic undersampling layer, to obtain the optimal undersampling pattern and its probability distribution in a differentiable way.
Double Weighted Truncated Nuclear Norm Regularization for Low-Rank Matrix Completion
The truncated nuclear norm regularization (TNNR) method is applicable in real-world scenarios.
Truncated nuclear norm regularization for low-rank tensor completion
It is proved that the recently proposed truncated nuclear norm (TNN) can replace the traditional nuclear norm, as an improved approximation to the rank of a matrix.
Low-Rank Tensor Completion by Truncated Nuclear Norm Regularization
Currently, low-rank tensor completion has gained cumulative attention in recovering incomplete visual data whose partial elements are missing.
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