A patch-based low-rank tensor approximation model for multiframe image denoising
From MaRDI portal
Publication:1675367
DOI10.1016/j.cam.2017.01.022zbMath1372.94107OpenAlexW2579595152MaRDI QIDQ1675367
Publication date: 27 October 2017
Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.cam.2017.01.022
Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)
Related Items (2)
Unfoldings and the rank-one approximation of the tensor ⋮ MRI denoising via sparse tensors with reweighted regularization
Uses Software
Cites Work
- Tensor Decompositions and Applications
- Parallel matrix factorization for low-rank tensor completion
- Fixed point and Bregman iterative methods for matrix rank minimization
- An alternating direction algorithm for matrix completion with nonnegative factors
- Solving a low-rank factorization model for matrix completion by a nonlinear successive over-relaxation algorithm
- Exact matrix completion via convex optimization
- Improved Iteratively Reweighted Least Squares for Unconstrained Smoothed $\ell_q$ Minimization
- Robust Low-Rank Tensor Recovery: Models and Algorithms
- Robust principal component analysis?
- A Singular Value Thresholding Algorithm for Matrix Completion
- Nonlocal Transform-Domain Filter for Volumetric Data Denoising and Reconstruction
- Nonlocal Image Restoration With Bilateral Variance Estimation: A Low-Rank Approach
- A Review of Image Denoising Algorithms, with a New One
- Augmented Lagrangian alternating direction method for matrix separation based on low-rank factorization
This page was built for publication: A patch-based low-rank tensor approximation model for multiframe image denoising
