In this paper, a mixed-order image denoising algorithm containing fractional-order and high-
order regularization terms is proposed, which effectively suppresses the staircase effect …

The primal-dual hybrid gradient (PDHG) algorithm is popular in solving min-max problems
which are being widely used in a variety of areas. To improve the applicability and efficiency …

We consider a class of saddle point problems frequently arising in the areas of image
processing and machine learning. In this paper, we propose a simple primal-dual algorithm …

Image segmentation is a fundamental task in computer vision and image processing. How to
efficiently decrease the effect such as high noise, low resolution and intensity inhomogeneity …

In this paper, we design an inertial accelerated primal–dual algorithm to address the convex–
concave saddle point problem, which is formulated as min x max yf (x)+< K x, y>− g (y) …

In this paper, we design first-order primal-dual algorithms to address the convex-concave
saddle point problem, which is formulated as $\min_ {x}\max_ {y} f (x)+\langle Kx, y\rangle-g …

In this paper, we study the linear convergence of several well-known first-order primal-dual
methods for solving a class of convex-concave saddle point problems. We first unify the …

By exploiting double-penalty terms for the primal subproblem, we develop a novel relaxed
augmented Lagrangian method for solving a family of convex optimization problems subject …

Adopting a suitable approximation strategy can both enhance the robustness and improve
the efficiency of the numerical algorithms. In this paper, we suggest combining two …

The total variation infimal convolution (TV-IC) model combining Kullback–Leibler and ℓ 2-
norm data fidelity term works well for the inverse problem of mixed Poisson–Gaussian noise …