evaluate_image_opt.m

量子遗传算法用于图象处理 量子遗传算法用于图象处理

function [best_index,best_fitness,best_alpha,best_beta]=evaluate_image_opt(r,g_cameraman,row,colume,num_pixel,num_of_singles,num_of_chrom,num_of_genes)
dec=zeros(1,num_of_singles*num_of_chrom);
flt=zeros(1,num_of_singles*num_of_chrom);
for i=1:num_of_singles
    for j=1:num_of_chrom
        curr_chrom=r((2*i-3+j)*num_of_genes+1:(2*i-2+j)*num_of_genes);
        for k=1:num_of_genes
            dec(i*2+j-2)=dec(i*2+j-2)+curr_chrom(k)*2^(num_of_genes-k);
        end
    end
end
flt=round(10*(dec/1024));
fitness=zeros(1,num_of_singles);
for k=1:num_of_singles
    alpha=flt(k*2-1);
    beta=flt(k*2);
    if alpha==0
       alpha=1;
    end
    if beta==0
        beta=1;
    end
    gc_cameraman=zeros(row,colume);
    syms t;
    for i=1:row
        for j=1:colume
            gc_cameraman(i,j)=double(int(t^(alpha-1)*(1-t)^(beta-1),t,0,g_cameraman(i,j))/int(t^(alpha-1)*(1-t)^(beta-1),t,0,1));
        end
    end
    fitness(k)=sum(sum(gc_cameraman.*gc_cameraman))/num_pixel-(sum(sum(gc_cameraman))/num_pixel)^2;
end
[best_fitness best_index]=max(fitness);
best_alpha=flt(best_index*2-1);
best_beta=flt(best_index*2);