基于matlab暗通道图像去雾

ubeautqq · 发表于 2021-6-3 14:54

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一、简介

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。

二、源代码

**
%--------------------------------------
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;**: V1 s: d: q V