Stable local-smooth principal component pursuit

Computing methodologies for image processing (68U10) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Numerical methods for low-rank matrix approximation; matrix compression (65F55)

Cites Work

Unnamed Item
Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
Nearly unbiased variable selection under minimax concave penalty
Latent variable graphical model selection via convex optimization
Noisy matrix decomposition via convex relaxation: optimal rates in high dimensions
Robust functional principal components: a projection-pursuit approach
An algorithm for total variation minimization and applications
Bridging convex and nonconvex optimization in robust PCA: noise, outliers and missing data
Compressive total variation for image reconstruction and restoration
Stable Image Reconstruction Using Total Variation Minimization
Robust principal component analysis?
Square-root lasso: pivotal recovery of sparse signals via conic programming
A New Alternating Minimization Algorithm for Total Variation Image Reconstruction
A New Total Variation Method for Multiplicative Noise Removal
Probabilistic Principal Component Analysis
Sparse Signal Estimation by Maximally Sparse Convex Optimization
Guarantees of total variation minimization for signal recovery
Nonconvex Nonsmooth Low Rank Minimization via Iteratively Reweighted Nuclear Norm
Total Variation Regularized Tensor RPCA for Background Subtraction From Compressive Measurements
Accelerated Alternating Projections for Robust Principal Component Analysis
Lattice-Based Patterned Fabric Inspection by Using Total Variation with Sparsity and Low-Rank Representations
De-noising by soft-thresholding
Iterative Methods for Total Variation Denoising
Compressive principal component pursuit
An Unbiased Approach to Low Rank Recovery
Enhanced 3DTV Regularization and Its Applications on HSI Denoising and Compressed Sensing
Inference and uncertainty quantification for noisy matrix completion
Truncated Nuclear Norm Minimization Based Group Sparse Representation for Image Restoration
Finding Low-Rank Solutions via Nonconvex Matrix Factorization, Efficiently and Provably
Stable Principal Component Pursuit via Convex Analysis
Near-Optimal Compressed Sensing Guarantees for Total Variation Minimization
Total Variation Structured Total Least Squares Method for Image Restoration

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