Image Filter for Removal of Impulse Noise Based on Directional Derivatives

One of the most common image processing tasks involves the removal of noise from images. Noise can be introduced during image capture, during transmission, or during storage. For design purposes, noise sources are frequently approximated by random variables with a known probability distribution. One common noise model corrupts a signal by introducing impulses. And the surface of the image disturbed by impulse noise displays many peaks or vales. According to the characteristic of impulse noise, a novel algorithm is proposed to the detection of impulse noise point from images based on directional derivatives. First, the theory of calculus on directional derivatives is introduced in detail. Then it is applied to the field of image to removing noise with the discrete form derived from its continuous mathematical model. And a number of contrasting simulations illustrate that our algorithm not only can preserve the structure information while removing impulse noise but also can mostly save the gray value of the pixels undisturbed by noise. In addition, the comparisons of the filtering performance for removing impulse noise are analyzed in detail in the case of different noise densities, and also show that the algorithm suggested outperforms the conventional filter algorithms such as mean filter, median filter and so on in speed and impulse noise reduction, especially in random-valued impulse noise reduction. So it is a very good alternative to the existing schemes.

Relations of 3D directional derivatives and expressions of typical differential operators

Relations of the 3D multi-directional derivatives are studied in this paper. These relations are applied to a geeral second-order linear elliptical operator and the corresponding expression are obtained. These relations and expressions play important roles in the meshless finite point method.

Extremum of second-order directional derivatives

In this paper, the extremum of second-order directional derivatives, i.e. the gradient of first-order derivatives is discussed. Given second-order directional derivatives in three nonparallel directions, or given second-order directional derivatives and mixed directional derivatives in two nonparallel directions, the formulae for the extremum of second-order directional derivatives are derived, and the directions corresponding to maximum and minimum are perpendicular to each other.

Directional Derivatives of Difference Functions of Nonsmooth Max-functions

In this paper, we give an upper estimate for the Clarke-Rockafellar directional derivatives of a function of the form f - g, where f, g are max-functions defined by locally Lipschitz but not necessarily differentiable functions on a closed convex set in a Euclidean space. As an application, we give a sufficient condition for f - g to have an error bound.

Relations between Directional Derivatives for Smooth Functions in 2D and 3D Spaces

In this paper, relations between directional derivatives are considered for smooth functions both in 2D and 3D spaces. These relations are established in the form of linear combinations of directional derivatives with their coefficients having simple form and structural regularity. By them, expressions based on directional derivatives for some typical differential operators are derived. This builds up a solid mathematical foundation for further study on numerical computation by the finite point method based on directional difference.

FEATURE PRESERVING IMAGE INTERPOLATION BY ADAPTIVE BIDIRECTIONAL FLOW

Most image interpolation algorithms currently used suffer visually to some extent the effects of blurred edges and jagged artifacts in the image. This letter presents an adaptive feature preserving bidirectional flow process, where an inverse diffusion is performed to enhance edges along the normal directions to the iso-phote lines (edges), while a normal diffusion is done to remove artifacts ('jaggies') along the tangent directions. In order to preserve image features such as edges, angles and textures, the nonlinear diffusion coefficients are locally adjusted according to the first order and the second order directional derivatives of the image. Experimental results on the Lena image demonstrate that our interpolation algorithm substantially improves the subjective quality of the interpolated images over conventional interpolations.

Importance Analysis of a Multi-state System Based on Direct Partial Logic Derivatives and Multi-valued Decision Diagrams

Importance analysis quantifies the critical degree of individual component. Compared with the traditional binary state system,importance analysis of the multi-state system is more aligned with the practice. Because the multi-valued decision diagram( MDD) can reflect the relationship between the components and the system state bilaterally, it was introduced into the reliability calculation of the multi-state system( MSS). The building method,simplified criteria,and path search and probability algorithm of MSS structure function MDD were given,and the reliability of the system was calculated. The computing methods of importance based on MDD and direct partial logic derivatives( DPLD) were presented. The diesel engine fuel supply system was taken as an example to illustrate the proposed method. The results show that not only the probability of the system in each state can be easily obtained,but also the influence degree of each component and its state on the system reliability can be obtained,which is conducive to the condition monitoring and structure optimization of the system.

BrCo (dmgh)_2Py/Zn Promoted Addition of BrCF_2CF_2CH=CHCH_2X to Alkynes: Direct Synthesis of 4,4,5,5-Tetrafluorocyclopentene Derivatives

BrCF2CF2CH=CHCH2X(x=Cl, OAc, OH) reacted smoothly with alkynes in the presence of BrCo(dmgh)2Py/Zn, giving 4,4,5,5-tetrafluorocyclopentene derivatives in moderate yields.

THE TANGENT CONES ON CONSTRAINT QUALIFICATIONS IN OPTIMIZATION PROBLEMS

This article proposes a few tangent cones,which are relative to the constraint qualifications of optimization problems.With the upper and lower directional derivatives of an objective function,the characteristics of cones on the constraint qualifications are presented.The interrelations among the constraint qualifications,a few cones involved, and level sets of upper and lower directional derivatives are derived.

LEARNING RATES OF KERNEL-BASED ROBUST CLASSIFICATION

This paper considers a robust kernel regularized classification algorithm with a non-convex loss function which is proposed to alleviate the performance deterioration caused by the outliers.A comparison relationship between the excess misclassification error and the excess generalization error is provided;from this,along with the convex analysis theory,a kind of learning rate is derived.The results show that the performance of the classifier is effected by the outliers,and the extent of impact can be controlled by choosing the homotopy parameters properly.

A TRUST REGION ALGORITHM VIA BILEVEL LINEAR PROGRAMMING FOR SOLVING THE GENERAL MULTICOMMODITY MINIMAL COST FLOW PROBLEMS

This paper proposes a nonmonotonic backtracking trust region algorithm via bilevel linear programming for solving the general multicommodity minimal cost flow problems.Using the duality theory of the linear programming and convex theory,the generalized directional derivative of the general multicommodity minimal cost flow problems is derived.The global convergence and superlinear convergence rate of the proposed algorithm are established under some mild conditions.

ONLINE REGULARIZED GENERALIZED GRADIENT CLASSIFICATION ALGORITHMS

This paper considers online classification learning algorithms for regularized classification schemes with generalized gradient. A novel capacity independent approach is presented. It verifies the strong convergence of sizes and yields satisfactory convergence rates for polynomially decaying step sizes. Compared with the gradient schemes, this al- gorithm needs only less additional assumptions on the loss function and derives a stronger result with respect to the choice of step sizes and the regularization parameters.

On Second-order Sufficient Conditions in Constrained Nonsmooth Optimization

In this paper, we establish a second-order sufficient condition for constrained optimization problems of a class of so called t-stable functions in terms of the first-order and the second-order Dini type directional derivatives. The result extends the corresponding result of [D. Bednarik and K. Pastor, Math. Program. Ser. A, 113（2008）, 283-298] to constrained optimization problems.

DIRECT DETERMINATION OF NEODYMIUM AND ERBIUM IN RARE EARTH MIXTURE BY DERIVATIVE SPECTROPHOTOMETRY

The absorption spectra of 4f electron transitions of the complexes of neodymium and erbium with 8-hydroxyquinoline-5-sulphonic acid in the presence of diethylamine and ethanol have been measured by normal and third-derivative spectrophotometry. Their molar absorptivities are 70.7 l.mol^(-1).cm^(-1) for Nd and 62.5 l.mol^(-1).cm^(-1) for Er. They are 7.6 times and 14.9 times greater than those of corresponding chlorides, respectively. Use of the third-derivative spectra both eliminates the interference of Ce(Ⅳ) and increases the sensitivity for Nd and Er. Beer,s Law was obeyed from 0-10 ug/ml of Nd and Er. The method has been applied to the determination of neodymium and erbium in rare earth mixtures.

Fast Evaluation Algorithm for NURBS Surfaces

Through a precise recursion of B-spline bases and the resursive expression of the derivatives of rational surfaces, this paper presents an efficient algorithm for the calculation of NURBS surfaces and all their directional derivatives. The algorithm requires less storage and proves to be stable.

TWO ALGORITHMS FOR LC^1 UNCONSTRAINED OPTIMIZATION

Presents two algorithms for LC unconstrained optimization problems which use the second order Dini upper directional derivative. Simplicity of the methods to use and perform; Discussion of related properties of the iteration function.

On Relations of Vector Optimization Results with C^(1,1) Data

In this article we prove that some of the sufficient and necessary optimality conditions obtained by Ginchev, Guerraggio, Luc [Appl. Math., 51, 5-36 （2006）] generalize （strictly） those presented by Guerraggio, Luc [J. Optim. Theory Appl., 109, 615-629 （2001）]. While the former paper shows examples for which the conditions given there are effective but the ones from the latter paper fail, it does not prove that generally the conditions it proposes are stronger. In the present note we complete this comparison with the lacking proof.

OPTIMALITY CONDITIONS OF A CLASS OF SPECIAL NONSMOOTH PROGRAMMING

In this paper, we investigate the optimality conditions of a class of special nonsmoothprogramming min F(x) = which arises from Li-norm optimization,where ci ∈ R is constant and fi ∈ C1,i = 1,2, ...,m. These conditions can easily be tested by computer.

A GENERALIZED GRADIENT PROJECTION METHOD FOR OPTIMIZATION PROBLEMS WITH EQUALITY AND INEQUALITY CONSTRAINTS ABOUT ARBITRARY INITIAL POINT

In this paper, we give a new generalized gradient projection algorithm for nonlinear optimization problems with arbitrary initial point. This new algorithm has some important advantages as follows: (1) The algorithm does not require initial feasible point; (2) It can deal with nonlinear equality and inequality constraints problems; (3) The structure of our algorithm is very simple;(4) Under some mild assumptions, it has global convergence.