Non-blind and blind deconvolution under poisson noise using fractional-order total variation
In a wide range of applications such as astronomy, biology, and medical imaging, acquired
data are usually corrupted by Poisson noise and blurring artifacts. Poisson noise often …
data are usually corrupted by Poisson noise and blurring artifacts. Poisson noise often …
A hybrid study of a 4-stage compressed solar distiller based on experimental, computational and deep learning methods
RA Ardekani, A Kianifar, MM Ghafurian - Desalination, 2023 - Elsevier
In the present study, a hybrid technique is employed to demonstrate the thermal
performance of a 4-stage compressed solar distiller with hydrophilic evaporators (CSDHE) …
performance of a 4-stage compressed solar distiller with hydrophilic evaporators (CSDHE) …
Convergence rate of overlap** domain decomposition methods for the Rudin--Osher--Fatemi model based on a dual formulation
This paper is concerned with overlap** domain decomposition methods (DDMs), based
on successive subspace correction (SSC) and parallel subspace correction (PSC), for the …
on successive subspace correction (SSC) and parallel subspace correction (PSC), for the …
Perception over time: Temporal dynamics for robust image understanding
While deep learning surpasses human-level performance in specific vision tasks, it is fragile
and overconfident in its classification. For example, minor transformations in perspective …
and overconfident in its classification. For example, minor transformations in perspective …
Domain Decomposition Methods Using Dual Conversion for the Total Variation Minimization with Fidelity Term
Nowadays, as large scale images become available, the necessity of parallel algorithms for
image processing has been arisen. In this paper, we propose domain decomposition …
image processing has been arisen. In this paper, we propose domain decomposition …
Convergent non-overlap** domain decomposition methods for variational image segmentation
This paper concerns with the non-overlap** domain decomposition methods (DDMs) for
the Chan–Vese model in variational image segmentation. We work with a saddle point …
the Chan–Vese model in variational image segmentation. We work with a saddle point …
Circulant preconditioners for mean curvature-based image deblurring problem
The mean curvature-based image deblurring model is widely used to enhance the quality of
the deblurred images. However, the discretization of the associated Euler–Lagrange …
the deblurred images. However, the discretization of the associated Euler–Lagrange …
Efficient Parallel Algorithms for Inpainting-Based Representations of 4K Images--Part I: Homogeneous Diffusion Inpainting
N Kämper, V Chizhov, J Weickert - arxiv preprint arxiv:2401.06744, 2024 - arxiv.org
In recent years inpainting-based compression methods have been shown to be a viable
alternative to classical codecs such as JPEG and JPEG2000. Unlike transform-based …
alternative to classical codecs such as JPEG and JPEG2000. Unlike transform-based …
A blocking scheme for dimension-robust Gibbs sampling in large-scale image deblurring
J Adams, M Morzfeld, K Joyce, M Howard… - Inverse Problems in …, 2021 - Taylor & Francis
Among the most significant challenges with using Markov chain Monte Carlo (MCMC)
methods for sampling from the posterior distributions of Bayesian inverse problems is the …
methods for sampling from the posterior distributions of Bayesian inverse problems is the …
Domain decomposition for non-smooth (in particular TV) minimization
A Langer - Handbook of Mathematical Models and Algorithms in …, 2021 - Springer
Abstract Domain decomposition is one of the most efficient techniques to derive efficient
methods for large-scale problems. In this chapter such decomposition methods for the …
methods for large-scale problems. In this chapter such decomposition methods for the …