Nonlocal image denoising via adaptive tensor nuclear norm minimization
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Summary
This paper model nonlocal similar patches through the multi-linear approach and proposes two tensor-based methods for image denoising based on the study of low-rank tensor estimation (LRTE), which are exceeding the state-of-the-art methods both visually and quantitatively.
- Type
- article
- Published
- 2015-09-16
- Cited by
- 36
- References
- 47
- OpenAlex
- https://openalex.org/W1438381303
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:30348854
Keywords
Singular value decomposition, Tensor (intrinsic definition), Matrix norm, Mathematics, Singular value
References
- Low-Rank Matrix Recovery via Efficient Schatten p-Norm Minimization
- A collaborative framework for 3D alignment and classification of heterogeneous subvolumes in cryo-electron tomography
- Scalable Tensor Factorizations for Incomplete Data
- Symmetrizing Smoothing Filters
- Fast and Accurate Matrix Completion via Truncated Nuclear Norm Regularization
- Nonlocally Centralized Sparse Representation for Image Restoration
- Third-Order Tensors as Operators on Matrices: A Theoretical and Computational Framework with Applications in Imaging
- Is Denoising Dead?
- A Tour of Modern Image Filtering: New Insights and Methods, Both Practical and Theoretical
- An Iterative Regularization Method for Total Variation-Based Image Restoration
- Reduction in impulse noise in digital images through a new adaptive artificial neural network model
- A Multilinear Singular Value Decomposition
- Nonlocal Image Restoration With Bilateral Variance Estimation: A Low-Rank Approach
- OptShrink: An Algorithm for Improved Low-Rank Signal Matrix Denoising by Optimal, Data-Driven Singular Value Shrinkage
- Tensor Decompositions and Applications
- Learning with tensors: a framework based on convex optimization and spectral regularization
- Novel Methods for Multilinear Data Completion and De-noising Based on Tensor-SVD
- Automatically improving image quality using tensor voting
- Weighted Nuclear Norm Minimization with Application to Image Denoising
- Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
Cited by
- Adaptive hexagonal fuzzy hybrid filter for Rician noise removal in MRI images
- Rank minimization with applications to image noise removal
- Medical image denoising based on 2D discrete cosine transform via ant colony optimization
- Deep CNN based MR image denoising for tumor segmentation using watershed transform
- MF-LRTC: Multi-filters guided low-rank tensor coding for image restoration
- Truncated nuclear norm regularization for low-rank tensor completion
- T-Jordan Canonical Form and T-Drazin Inverse Based on the T-Product
- Image Denoising Based on Nonlocal Bayesian Singular Value Thresholding and Stein’s Unbiased Risk Estimator
- Hyperspectral Image Denoising Using Global Weighted Tensor Norm Minimum and Nonlocal Low-Rank Approximation
- Generalized tensor function via the tensor singular value decomposition based on the T-product
- Image denoising via structure-constrained low-rank approximation
- Improved robust tensor principal component analysis for accelerating dynamic MR imaging reconstruction
- T-positive semidefiniteness of third-order symmetric tensors and T-semidefinite programming
- Hyperspectral Image Denoising Based on Nonlocal Low-Rank and TV Regularization
- Weighted Nuclear Norm Minimization-Based Regularization Method for Image Restoration
- Tensor extrapolation methods with applications
- SURE Based Truncated Tensor Nuclear Norm Regularization for Low Rank Tensor Completion
- High-Order Multilinear Discriminant Analysis via Order-n Tensor Eigendecomposition
- Learning-based low-rank denoising
- On sketch-and-project methods for solving tensor equations
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