Simultaneous image enhancement and restoration with non-convex total variation
DOI10.1007/S10915-021-01488-XOpenAlexW3159321614WikidataQ113106889 ScholiaQ113106889MaRDI QIDQ2031857
Publication date: 15 June 2021
Published in: Journal of Scientific Computing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10915-021-01488-x
image enhancementimage restorationretinexiteratively reweighted \(\ell_1\) algorithmalternating minimization algorithmnon-convex total variation
Numerical optimization and variational techniques (65K10) Computing methodologies for image processing (68U10) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)
Related Items (2)
Cites Work
- Unnamed Item
- Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
- Nonlinear total variation based noise removal algorithms
- Retinex by higher order total variation \(L^1\) decomposition
- An algorithm for total variation minimization and applications
- Retinex based on exponent-type total variation scheme
- A variational framework for retinex
- A first-order primal-dual algorithm for convex problems with applications to imaging
- A TV Bregman iterative model of Retinex theory
- Fast dual minimization of the vectorial total variation norm and applications to color image processing
- A variational model with hybrid hyper-Laplacian priors for Retinex
- A Total Variation Model for Retinex
- Truncated $l_{1-2}$ Models for Sparse Recovery and Rank Minimization
- Proximal Alternating Minimization and Projection Methods for Nonconvex Problems: An Approach Based on the Kurdyka-Łojasiewicz Inequality
- The Split Bregman Method for L1-Regularized Problems
- A Variational Model with Barrier Functionals for Retinex
- Alternating Direction Method of Multipliers for Nonlinear Image Restoration Problems
- Structure-Revealing Low-Light Image Enhancement Via Robust Retinex Model
- LIME: Low-Light Image Enhancement via Illumination Map Estimation
- Contrast Enhancement Based on Intrinsic Image Decomposition
- LR3M: Robust Low-Light Enhancement via Low-Rank Regularized Retinex Model
- Non-Local Retinex---A Unifying Framework and Beyond
- On Iteratively Reweighted Algorithms for Nonsmooth Nonconvex Optimization in Computer Vision
- A Nonlocal Total Variation Model for Image Decomposition: Illumination and Reflectance
- A PDE Formalization of Retinex Theory
- Scale Space and PDE Methods in Computer Vision
This page was built for publication: Simultaneous image enhancement and restoration with non-convex total variation