【图像分割】基于灰狼算法优化Renyi熵实现图像多阈值分割附Matlab代码

1 内容介绍

在图像阈值分割方法中,Renyi熵法因其显著效能而得到大量应用.为了更好地发挥Renyi熵在图像分割中的应用,提出把Renyi熵法扩展到图像多级阈值化问题.然而,由于计算时间复杂度上的高要求,很难把这种有效的技术推广到复杂图像多级阈值化问题.为减少本方法的计算时间,应用灰狼优化算法实施最佳阈值的搜索.实验结果表明,本方法能有效地对图像进行多级分割,并且显著降低计算时间.

2 部分代码

% Grey Wolf Optimizer

function [Alpha_score,Alpha_pos,Convergence_curve]=GWO(SearchAgents_no,Max_iter,lb,ub,dim,fhandle,fnonlin)

% initialize alpha, beta, and delta_pos

Alpha_pos=zeros(1,dim);

Alpha_score=inf; %change this to -inf for maximization problems

Beta_pos=zeros(1,dim);

Beta_score=inf; %change this to -inf for maximization problems

Delta_pos=zeros(1,dim);

Delta_score=inf; %change this to -inf for maximization problems

%Initialize the positions of search agents

Positions=initialization(SearchAgents_no,ub,lb);

Convergence_curve=zeros(1,Max_iter);

l=0;% Loop counter

% Main loop

while l<Max_iter

    for i=1:size(Positions,1)  

        

       % Return back the search agents that go beyond the boundaries of the search space

        Flag4ub=Positions(i,:)>ub;

        Flag4lb=Positions(i,:)<lb;

        Positions(i,:)=(Positions(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;               

        

        %% Calculate objective function for each search agent

        fitness=Fun(fhandle,fnonlin,Positions(i,:));  

        

        %% Update Alpha, Beta, and Delta

        if fitness<Alpha_score 

            Alpha_score=fitness; % Update alpha

            Alpha_pos=Positions(i,:);

        end

        

        if fitness>Alpha_score && fitness<Beta_score 

            Beta_score=fitness; % Update beta

            Beta_pos=Positions(i,:);

        end

        

        if fitness>Alpha_score && fitness>Beta_score && fitness<Delta_score 

            Delta_score=fitness; % Update delta

            Delta_pos=Positions(i,:);

        end

    end

    

    

    a=2-l*((2)/Max_iter); % a decreases linearly fron 2 to 0

    

    % Update the Position of search agents including omegas

    for i=1:size(Positions,1)

        for j=1:size(Positions,2)     

                       

            r1=rand(); % r1 is a random number in [0,1]

            r2=rand(); % r2 is a random number in [0,1]

            

            A1=2*a*r1-a; % Equation (3.3)

            C1=2*r2; % Equation (3.4)

            

            D_alpha=abs(C1*Alpha_pos(j)-Positions(i,j)); % Equation (3.5)-part 1

            X1=Alpha_pos(j)-A1*D_alpha; % Equation (3.6)-part 1

                       

            r1=rand();

            r2=rand();

            

            A2=2*a*r1-a; % Equation (3.3)

            C2=2*r2; % Equation (3.4)

            

            D_beta=abs(C2*Beta_pos(j)-Positions(i,j)); % Equation (3.5)-part 2

            X2=Beta_pos(j)-A2*D_beta; % Equation (3.6)-part 2       

            

            r1=rand();

            r2=rand(); 

            

            A3=2*a*r1-a; % Equation (3.3)

            C3=2*r2; % Equation (3.4)

            

            D_delta=abs(C3*Delta_pos(j)-Positions(i,j)); % Equation (3.5)-part 3

            X3=Delta_pos(j)-A3*D_delta; % Equation (3.5)-part 3             

            

            Positions(i,j)=(X1+X2+X3)/3;% Equation (3.7)

            

        end

    end

    l=l+1;    

    Convergence_curve(l)=Alpha_score;

end

3 运行结果

4 参考文献

[1]聂方彦, 张平凤, 潘梅森,等. 基于Renyi熵与PSO算法的图像多级阈值分割[J]. 湖南文理学院学报：自然科学版, 2013, 25(3):6.

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