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 

%%%%%%%%%%%%%%% 这段没看懂什么意思 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),... col_inds(1+len:neb_size^2+len)=reshape(repmat(win_inds',neb_size,1),... neb_size^2,1); 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);