Hyperspectral image (HSI) denoising is challenging not only because of the difficulty in preserving both spectral and spatial structures simultaneously, but also due to requirement removing various noises, which are often mixed together. In this paper, we present a nonconvex low rank matrix approximation (NonLRMA) model corresponding HSI method by reformulating problem using regularizer instead traditional nuclear norm, resulting tighter original sparsity-regularised function. NonLRMA aims...

Distributed computation over networks is now receiving an increasing attention in many fields such as engineering and machine learning, where the solution of a linear system equations basic task. This paper presents asynchronous distributed randomized block Kaczmarz projection algorithm for solving large-scale systems multi-agent networks, each agent only holds part problem data. An event-triggered communication mechanism integrated to minimize overhead reduce overall costs. allows update...

Restoration of hyperspectral images (HSIs) is a challenging task, owing to the reason that are inevitably contaminated by mixture noise, including Gaussian impulse dead lines, and stripes, during their acquisition process. Recently, HSI denoising approaches based on low-rank matrix approximation have become an active research field in remote sensing achieved state-of-the-art performance. These approaches, however, unavoidably require calculate full or partial singular value decomposition...

Reconstruction of highly accelerated dynamic magnetic resonance imaging (MRI) is crucial importance for the medical diagnosis. The application general robust principal component analysis (RPCA) to MRI can increase speed and efficiency. However, conventional RPCA makes use nuclear norm as convex surrogate rank function, whose drawbacks have been mentioned in plenty literature. Recently, nonconvex surrogates function widely investigated proved be tighter approximation than by massive...

Background/foreground separation has become an inevitable step in numerous image/video processing applications, such as inpainting, anomaly detection, motion segmentation, augmented reality, and so on. Recent low-rank based approaches, robust principal component analysis separating a data matrix into with sparse matrix, have achieved encouraging performance. However, these approaches usually need relatively high computation cost, mainly due to calculation of full or partial singular value...

Recently, robust principal component analysis (RPCA) has been widely used in the detection of moving objects. However, this method fails to effectively utilize low-rank prior information background and spatiotemporal continuity object, target extraction effect is often poor when dealing with large-scale complex scenes. To solve above problems, a new non-convex rank approximate RPCA model based on segmentation constraint proposed. Firstly, adopts sparse decomposition divide original video...

We propose a monotone descent active set QP-free method for inequality constrained optimization that ensures the feasibility of all iterates and allows on boundary feasible set. The study is motivated by Facchinei--Fischer--Kanzow identification technique nonlinear programming variational inequalities [F. Facchinei, A. Fischer, C. Kanzow, SIAM J. Optim., 9 (1999), pp. 14-32]. Distinguishing features proposed compared with existing methods include lower subproblem costs fast convergence rate...

The generalized trigonometric functions which have a short history, were introduced by Lindqvist two decades ago.Since 2012, many mathematician began to study their classical inequalities, general convexity and concavity, multiple-angle formulas parameter concavity.A number of results been obtained.This is survey.Some new refinements, generalizations, applications, related problems are summarized.

In this paper, we establish a concave theorem and some inequalities for the generalized digamma function. Hence, give complete monotonicity property of determinant function involving all kinds derivatives

Traditional robust principal component analysis (RPCA) is very prone to voids in the process of background/foreground separation complex scene videos and easy misjudge dynamic background as a moving target, which makes effect unideal.In order address this problem, article introduces super-pixel segmentation technique into RPCA model.First, Linear Spectral Clustering algorithm (LSC) used mark video sequence grouping matrix obtained.Then new model proposed based on non-convex rank...

In this paper, we show an elegant inequality involving the ratio of generalized complete elliptic integrals first kind and generalize interesting result Alzer.

SUMMARY A three-dimensional (3D) model of a novel 5-DoF type parallel manipulator with couple-constrained wrench is constructed and its analyzed. First, the formulas are derived for solving displacement, velocity, acceleration moving platform links, workspace constructed. Second, inertial wrenches links. Third, dynamics equation established by considering friction, dynamically active forces wrench. Finally, numerical example given to demonstrate analytic solution kinematics dynamics,...

The authors obtain some Wilker and Cusa type inequalities for generalized trigonometric hyperbolic functions generalize known inequalities.

This article introduces a smoothing technique to the l1 exact penalty function. An application of yields twice continuously differentiable function and smoothed problem. Under some mild conditions, optimal solution problem becomes an approximate original constrained optimization Based on problem, we propose algorithm solve Every limit point sequence generated by is solution. Several numerical examples are presented illustrate performance proposed algorithm.