Based on the advantages and disadvantages of the standard median filter and the standard wean filter, a new Adaptive Weighted Mean Filter（AWFM） was proposed in this paper. The filter window＇s size of every pixel wa...Based on the advantages and disadvantages of the standard median filter and the standard wean filter, a new Adaptive Weighted Mean Filter（AWFM） was proposed in this paper. The filter window＇s size of every pixel was adaptively adjusted. Then the suspidons noise points were examined by certain rules. After that, the authors calculate the weighting factors of the pixels by weighting function which was canstructed according to the differences between their gray values and the median value of all elements in the window. Finally they use the weighted average of gray values to substitute the gray value of the central pixel in the window. The results indicate that this filtering method is not only effective for impulse noise like median filter, but also better than the standard median filters. Compared with conventional filter, this filter methed can effectivdy suppress the mixture noise of images, and protect image＇s details well.展开更多
In order to exploit the enhancement of the multi- objective evolutionary algorithm based on decomposition （MOEA/D）, we propose an improved algorithm with uniform de- sign （UD）, i.e. MOEA/D-UD. Three mechanisms in ...In order to exploit the enhancement of the multi- objective evolutionary algorithm based on decomposition （MOEA/D）, we propose an improved algorithm with uniform de- sign （UD）, i.e. MOEA/D-UD. Three mechanisms in MOEA/D-UD are modified by introducing an experimental design method called UD. To fully employ the information contained in the domain of the multi-objective problem, we apply UD to initialize a uniformly scattered population. Then, motivated by the analysis of the re- lationship between weight vectors and optimal solutions of scalar subproblems in the study of MOEND with adaptive weight ad- justment （MOEA/D-AWA）, a new weight vector design method based on UD is introduced. To distinguish real sparse regions from pseudo sparse regions, i.e. discontinuous regions, of the complex Pareto front, the weight vector adjustment strategy in MOEMD-UD adequately utilizes the information from neighbors of individuals. In the experimental study, we compare MOEA/D-UD with three outstanding algorithms, namely MOEA/D with the dif- ferential evolution operator （MOEA/D-DE）, MOEA/D-AWA and the nondominated sorting genetic algorithm II （NSGA-II） on nineteen test instances. The experimental results show that MOEA/D-UD is capable of obtaining a well-converged and well diversified set of solutions within an acceptable execution time.展开更多
基金supported by the University Independent innovation program of Jinan(No.200906005)the National Natural Science Foundation of Shandong Province(No.Y2008G31)
文摘Based on the advantages and disadvantages of the standard median filter and the standard wean filter, a new Adaptive Weighted Mean Filter（AWFM） was proposed in this paper. The filter window＇s size of every pixel was adaptively adjusted. Then the suspidons noise points were examined by certain rules. After that, the authors calculate the weighting factors of the pixels by weighting function which was canstructed according to the differences between their gray values and the median value of all elements in the window. Finally they use the weighted average of gray values to substitute the gray value of the central pixel in the window. The results indicate that this filtering method is not only effective for impulse noise like median filter, but also better than the standard median filters. Compared with conventional filter, this filter methed can effectivdy suppress the mixture noise of images, and protect image＇s details well.
文摘In order to exploit the enhancement of the multi- objective evolutionary algorithm based on decomposition （MOEA/D）, we propose an improved algorithm with uniform de- sign （UD）, i.e. MOEA/D-UD. Three mechanisms in MOEA/D-UD are modified by introducing an experimental design method called UD. To fully employ the information contained in the domain of the multi-objective problem, we apply UD to initialize a uniformly scattered population. Then, motivated by the analysis of the re- lationship between weight vectors and optimal solutions of scalar subproblems in the study of MOEND with adaptive weight ad- justment （MOEA/D-AWA）, a new weight vector design method based on UD is introduced. To distinguish real sparse regions from pseudo sparse regions, i.e. discontinuous regions, of the complex Pareto front, the weight vector adjustment strategy in MOEMD-UD adequately utilizes the information from neighbors of individuals. In the experimental study, we compare MOEA/D-UD with three outstanding algorithms, namely MOEA/D with the dif- ferential evolution operator （MOEA/D-DE）, MOEA/D-AWA and the nondominated sorting genetic algorithm II （NSGA-II） on nineteen test instances. The experimental results show that MOEA/D-UD is capable of obtaining a well-converged and well diversified set of solutions within an acceptable execution time.
