[HTML][HTML] A non-convex denoising model for impulse and Gaussian noise mixture removing using bi-level parameter identification
We propose a new variational framework to remove a mixture of Gaussian and impulse
noise from images. This framework is based on a non-convex PDE-constrained with a …
noise from images. This framework is based on a non-convex PDE-constrained with a …
Recent advances in domain decomposition methods for total variation minimization
Total variation minimization is standard in mathematical imaging and there have been
numerous researches over the last decades. In order to process large-scale images in real …
numerous researches over the last decades. In order to process large-scale images in real …
A gradient smoothing method and its multiscale variant for flows in heterogeneous porous media
In this paper, we study gradient smoothing methods (GSMs) with improved convergence
behaviors for high-contrast problems such as the flow in heterogeneous porous media. We …
behaviors for high-contrast problems such as the flow in heterogeneous porous media. We …
Fast Non-overlapping Domain Decomposition Methods for Continuous Multi-phase Labeling Problem
Z Zhang, H Chang, Y Duan - Journal of Scientific Computing, 2023 - Springer
This paper presents the domain decomposition methods (DDMs) for achieving fast parallel
computing on multi-core computers when dealing with the multi-phase labeling problem. To …
computing on multi-core computers when dealing with the multi-phase labeling problem. To …
A general decomposition method for a convex problem related to total variation minimization
S Hilb, A Langer - arXiv preprint arXiv:2211.00101, 2022 - arxiv.org
We consider sequential and parallel decomposition methods for a dual problem of a general
total variation minimization problem with applications in several image processing tasks, like …
total variation minimization problem with applications in several image processing tasks, like …