The authors propose JLRST, a subspace-based unified tensor regularizer that jointly enforces low-rankness and local smoothness on gradient tensors of clustered coefficients, solved via ADMM with mode-3 logarithmic TNN.
Mixed noise removal in hyperspectral image via low-fibered-rank regularization,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
math.NA 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Low-rankness and Smoothness Meet Subspace: A Unified Tensor Regularization for Hyperspectral Image Super-resolution
The authors propose JLRST, a subspace-based unified tensor regularizer that jointly enforces low-rankness and local smoothness on gradient tensors of clustered coefficients, solved via ADMM with mode-3 logarithmic TNN.