暗通道去雾python 暗通道去雾matlab代码

基于matlab暗通道之图像去雾

上述伪代码中，I表示导向图像（guided image）,p为输入图像（input image）,q为输出图像（output image），表示均值滤波，r为窗口半径。

 

代码：

function R = anyuanse(m_img)
% 原始图像
I=double(m_img)/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);

win_dark=zeros(img_size ,1);

for cc=1:img_size
   win_dark(cc)=1;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5
win_dark=reshape(win_dark,h,w);
%计算分块深度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
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5
 %图像透射率预处理，深度图反相
 for cc=1:img_size
   win_dark(cc)=1-win_dark(cc);
 end
 %%%%%%%%%%%%%%%%%图像软抠图开始%%%%%%%%%%%%%%%%%%%%%
 %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)     %没有余数？
            if(rem(cj-8,15)<1)
                win_b(ci*w+cj)=win_dark(ci*w+cj);
            end
        end

    end
end

%显示分块darkchannel
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(:));
  %%%%%%%%%%%%%%%%%%%%%%%%%软图像抠图结束%%%%%%%%%%%%%%%55

   %去掉0-1范围以外的数
  alpha=max(min(reshape(x,h,w),1),0);%图像透射率

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
R = dehaze;