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Nonconvex Nonsmooth Low-Rank Minimization for Generalized Image Compressed Sensing via Group Sparse Representation
The popular L_2-norm and M-estimator are employed for standard image CS and robust CS problem to fit the data respectively.
From Group Sparse Coding to Rank Minimization: A Novel Denoising Model for Low-level Image Restoration
Recently, low-rank matrix recovery theory has been emerging as a significant progress for various image processing problems.
ADMM-IDNN: Iteratively Double-reweighted Nuclear Norm Algorithm for Group-prior based Nonconvex Compressed Sensing via ADMM
Group-prior based regularization method has led to great successes in various image processing tasks, which can usually be considered as a low-rank matrix minimization problem.
Image and Video Processing
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