基于暗通道去雾的matlab代码备注实现
这段代码对内存很高，所使用图片尽量控制在200*300范围内
img_name='002.bmp';
% 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);
%这段代码可用矩阵处理
for cc=1:img_size
win_dark(cc)=1;
end
win_dark=reshape(win_dark,h,w);
%计算分块darkchannel，此处也尝试用矩阵处理，循环太多
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);
%输出darkchannel图像
%%%%%%%%%%%%%%%
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);
%rem函数为求余函数，有可能是进行边缘处理
for ci=1:h
for cj=1:w
if(rem(ci-8,15)<1)
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
%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);
%是不是win_dark
%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(:)); %求解式15
%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);