TA的每日心情 | 衰 2019-11-19 15:32 |
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签到天数: 1 天 [LV.1]初来乍到
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一、简介
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& ]4 k; T. h2 _# o! i" S- M' E+ E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。& x3 C" Z4 w2 l& i- E( p
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二、源代码
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- %--------------------------------------
- clc;
- clear;
- close all;
- %% -----------图像去雾算法----------------
- %% 加载图片
- img = imread('foggy_bench.jpg');
- figure;imshow(img);title('雾图');
- %% 去雾函数
- De_img = anyuanse(img);
- figure;imshow(De_img);title('去雾图');
- %% 输出结果,分辨率300dpi并保存为tiff图片
- imwrite(De_img,'1.tiff','tiff','Resolution',300);
- function R = anyuanse(m_img)
- % 原始图像
- 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;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
- R = dehaze;**
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三、运行结果
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