www.pudn.com > Dark-Channel-Prior-Codes.rar > SoftMatting.m, change:2013-12-25,size:3188b


% 
img_name='6.png'; 
% 原始图像 
I=double(imread(img_name))/255; 
 
% 获取图像大小 
 
[h,w,c]=size(I); 
win_size = 7; 
img_size=w*h; 
dehaze=zeros(img_size*c,1); 
dehaze=reshape(dehaze,h,w,c); 
 
 
figure, imshow(I); 
win_dark=zeros(img_size ,1); 
 
for cc=1:img_size 
    win_dark(cc)=1; 
end 
 
win_dark=reshape(win_dark,h,w); 
%计算分块darkchannel 
for j=1+win_size:w-win_size 
    for i=win_size+1:h-win_size 
        m_pos_min = min(I(i,j,:)); 
        for n=j-win_size:j+win_size 
            for m=i-win_size:i+win_size 
                if(win_dark(m,n)>m_pos_min) 
                    win_dark(m,n)=m_pos_min; 
                end 
            end 
        end 
         
    end 
end 
 
figure, imshow(win_dark); 
 
for cc=1:img_size 
    win_dark(cc)=1-win_dark(cc); 
end 
%选定精确dark value坐标 
win_b = zeros(img_size,1); 
 
for ci=1:h 
    for cj=1:w 
        if(rem(ci-8,15)<1) 
            if(rem(cj-8,15)<1)           %这么做的目的是-在全矩阵中得到稀疏点--均匀分布在矩阵中 
                win_b(ci*w+cj)=win_dark(ci*w+cj); 
            end 
        end 
         
    end 
end 
 
%显示分块darkchannel 
%figure, imshow(win_dark); 
neb_size = 9; 
win_size = 1; 
epsilon = 0.0000001; 
%指定矩阵形状 
indsM=reshape([1:img_size],h,w); 
%计算矩阵L 
tlen = img_size*neb_size^2; 
row_inds=zeros(tlen ,1); 
col_inds=zeros(tlen,1); 
vals=zeros(tlen,1); 
len=0; 
for j=1+win_size:w-win_size 
    for i=win_size+1:h-win_size 
        if(rem(ci-8,15)<1) 
            if(rem(cj-8,15)<1) 
                continue; 
            end 
        end 
        win_inds=indsM(i-win_size:i+win_size,j-win_size:j+win_size); 
        win_inds=win_inds(:);%列显示 
        winI=I(i-win_size:i+win_size,j-win_size:j+win_size,:); 
        winI=reshape(winI,neb_size,c); %三个通道被拉平成为一个二维矩阵 3*9 
        win_mu=mean(winI,1)';  %求每一列的均值 如果第二个参数为2 则为求每一行的均值  //矩阵变向量 
        win_var=inv(winI'*winI/neb_size-win_mu*win_mu' +epsilon/neb_size*eye(c)); %求方差 
         
        winI=winI-repmat(win_mu',neb_size,1);%求离差 
        tvals=(1+winI*win_var*winI')/neb_size;% 求论文所指的矩阵L 
         
        row_inds(1+len:neb_size^2+len)=reshape(repmat(win_inds,1,neb_size),... 
            neb_size^2,1); 
        col_inds(1+len:neb_size^2+len)=reshape(repmat(win_inds',neb_size,1),... 
            neb_size^2,1); 
        vals(1+len:neb_size^2+len)=tvals(:); 
        len=len+neb_size^2; 
    end 
end 
 
 
 
 
 
vals=vals(1:len); 
row_inds=row_inds(1:len); 
col_inds=col_inds(1:len); 
%创建稀疏矩阵 
A=sparse(row_inds,col_inds,vals,img_size,img_size); 
%求行的总和 sumA为列向量 
sumA=sum(A,2); 
%spdiags(sumA(:),0,img_size,img_size) 创建img_size大小的稀疏矩阵其元素是sumA中的列元素放在由0指定的对角线位置上。 
A=spdiags(sumA(:),0,img_size,img_size)-A; 
 
 
%创建稀疏矩阵 
D=spdiags(win_b(:),0,img_size,img_size); 
lambda=1; 
x=(A+lambda*D)/(lambda*win_b(:).*win_b(:)); 
 
%去掉0-1范围以外的数 
alpha=max(min(reshape(x,h,w),1),0); 
 
 
figure, imshow(alpha); 
A=220/255; %大气光没有去计算 
%去雾 
 
for i=1:c 
    for j=1:h 
        for l=1:w 
            dehaze(j,l,i)=(I(j,l,i)-A)/alpha(j,l)+A; 
        end 
    end 
end 
 
figure, imshow(dehaze);