A new reweighted minimization algorithm for image deblurring
From MaRDI portal
Publication:2258646
DOI10.1186/1029-242X-2014-238zbMath1319.68237OpenAlexW2159391177WikidataQ59323583 ScholiaQ59323583MaRDI QIDQ2258646
Weiguo Li, Alun Dong, Boying Wu, Tian-tian Qiao
Publication date: 26 February 2015
Published in: Journal of Inequalities and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1186/1029-242x-2014-238
Computing methodologies for image processing (68U10) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)
Cites Work
- Nonlinear total variation based noise removal algorithms
- Image super-resolution by TV-regularization and Bregman iteration
- Linearized Bregman iterations for compressed sensing
- Convergence of the linearized Bregman iteration for ℓ₁-norm minimization
- Linearized Bregman Iterations for Frame-Based Image Deblurring
- The Split Bregman Method for L1-Regularized Problems
- Nonlocal Linear Image Regularization and Supervised Segmentation
- Augmented Lagrangian Method, Dual Methods, and Split Bregman Iteration for ROF, Vectorial TV, and High Order Models
- Local Strong Homogeneity of a Regularized Estimator
- De-noising by soft-thresholding
- Recovery of Blocky Images from Noisy and Blurred Data
- Reweighted $\ell_1$-Minimization for Sparse Solutions to Underdetermined Linear Systems
- Bregman Iterative Algorithms for $\ell_1$-Minimization with Applications to Compressed Sensing
- An Iterative Regularization Method for Total Variation-Based Image Restoration
- A Review of Image Denoising Algorithms, with a New One
This page was built for publication: A new reweighted minimization algorithm for image deblurring
