DOI10.1137/120894130zbMath1279.68327OpenAlexW2018575953WikidataQ115155153 ScholiaQ115155153MaRDI QIDQ2873279
Andreas Langer, Michael Hintermüller
Publication date: 23 January 2014
Published in: SIAM Journal on Imaging Sciences (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1137/120894130
Locally adaptive total variation for removing mixed Gaussian–impulse noise,
A non-convex non-smooth bi-level parameter learning for impulse and Gaussian noise mixture removing,
Block decomposition methods for total variation by primal-dual stitching,
A non-convex denoising model for impulse and Gaussian noise mixture removing using bi-level parameter identification,
A spatially adaptive hybrid total variation model for image restoration under Gaussian plus impulse noise,
Automated parameter selection in the ${L}^{1} \mbox{-} {L}^{2}$-TV model for removing Gaussian plus impulse noise,
Domain decomposition methods using dual conversion for the total variation minimization with \(L^1\) fidelity term,
Fast non-overlapping domain decomposition methods for continuous multi-phase labeling problem,
On a variational problem with a nonstandard growth functional and its applications to image processing,
A primal-dual finite element method for scalar and vectorial total variation minimization,
Fast Nonoverlapping Block Jacobi Method for the Dual Rudin--Osher--Fatemi Model,
An \(l_0\)-norm based color image deblurring model under mixed random-valued impulse and Gaussian noise,
Color image restoration with mixed Gaussian-Cauchy noise and blur,
Iterative regularization via dual diagonal descent,
A non-convex PDE-constrained denoising model for impulse and Gaussian noise mixture reduction,
Nonconvex model for mixing noise with fractional-order regularization,
A Finite Element Approach for the Dual Rudin--Osher--Fatemi Model and Its Nonoverlapping Domain Decomposition Methods,
Variational regularisation for inverse problems with imperfect forward operators and general noise models,
Edge detection with mixed noise based on maximum a posteriori approach,
Unnamed Item,
Primal Domain Decomposition Methods for the Total Variation Minimization, Based on Dual Decomposition,
Convergent non-overlapping domain decomposition methods for variational image segmentation,
Automated parameter selection for total variation minimization in image restoration,
Edge-guided TV p regularization for diffuse optical tomography based on radiative transport equation,
Non-overlapping domain decomposition methods for dual total variation based image denoising,
A finite element nonoverlapping domain decomposition method with Lagrange multipliers for the dual total variation minimizations,
A denoising model adapted for impulse and Gaussian noises using a constrained-PDE,
RECENT ADVANCES IN DOMAIN DECOMPOSITION METHODS FOR TOTAL VARIATION MINIMIZATION,
Overlapping Domain Decomposition Methods for Total Variation Denoising,
Analysis and automatic parameter selection of a variational model for mixed Gaussian and salt-and-pepper noise removal,
Convergence Rate of Overlapping Domain Decomposition Methods for the Rudin--Osher--Fatemi Model Based on a Dual Formulation,
Accelerated Iterative Regularization via Dual Diagonal Descent,
Infimal Convolution of Data Discrepancies for Mixed Noise Removal
