【MATLAB-Retinex图像增强算法的去雾技术】

续：【MATLAB-基于直方图优化的图像去雾技术】

参考书籍：计算机视觉与深度学习实战:以MATLAB、Python为工具，
主编：刘衍琦, 詹福宇， 王德建
北京：电子工业出版社，2019

Retinex图像增强算法可以平衡图像的灰度动态范围压缩、图像增强、图像颜色恒常三个指标，能够实现对雾图像的自适应增强。对R、G、B三通道使用Retinex算法再整合成新的图片，实现方式如下：

1 原图

2 MATLAB实现代码

% Retinex实现图像去雾
% 输入参数：
%  f——图像矩阵

% 输出参数：
%  In——结果图像

% 加载路径和所有文件
clc;clear;close all;
cd(fileparts(mfilename('fullpath')));
addpath(genpath(cd));
%提取图像的R、G、B分量
Path = '.\images';                   % 设置数据存放的文件夹路径
File = dir(fullfile(Path,'*.jpg'));  % 显示文件夹下所有符合后缀名为.txt文件的完整信息
FileNames = {File.name}';            % 提取符合后缀名为.txt的所有文件的文件名，转换为n行1列
f = imread(FileNames{1});
fr = f(:, :, 1);
fg = f(:, :, 2);
fb = f(:, :, 3);
%数据类型归一化
mr = mat2gray(im2double(fr));
mg = mat2gray(im2double(fg));
mb = mat2gray(im2double(fb));
%定义alpha参数
alpha = randi([80 100], 1)*20;
%定义模板大小
n = floor(min([size(f, 1) size(f, 2)])*0.5);
%计算中心
n1 = floor((n+1)/2);
for i = 1:n
    for j = 1:n
        %高斯函数
        b(i,j)  = exp(-((i-n1)^2+(j-n1)^2)/(4*alpha))/(pi*alpha);
    end
end
%卷积滤波
nr1 = imfilter(mr,b,'conv', 'replicate');
ng1 = imfilter(mg,b,'conv', 'replicate');
nb1 = imfilter(mb,b,'conv', 'replicate');
ur1 = log(nr1);
ug1 = log(ng1);
ub1 = log(nb1);
tr1 = log(mr);
tg1 = log(mg);
tb1 = log(mb);
yr1 = (tr1-ur1)/3;
yg1 = (tg1-ug1)/3;
yb1 = (tb1-ub1)/3;
%定义beta参数
beta = randi([80 100], 1)*1;
%定义模板大小
x = 32;
for i = 1:n
    for j = 1:n
        %高斯函数
        a(i,j)  = exp(-((i-n1)^2+(j-n1)^2)/(4*beta))/(6*pi*beta);
    end
end
%卷积滤波
nr2 = imfilter(mr,a,'conv', 'replicate');
ng2 = imfilter(mg,a,'conv', 'replicate');
nb2 = imfilter(mb,a,'conv', 'replicate');
ur2 = log(nr2);
ug2 = log(ng2);
ub2 = log(nb2);
tr2 = log(mr);
tg2 = log(mg);
tb2 = log(mb);
yr2 = (tr2-ur2)/3;
yg2 = (tg2-ug2)/3;
yb2 = (tb2-ub2)/3;
%定义eta参数
eta = randi([80 100], 1)*200;
for i = 1:n
    for j = 1:n
        %高斯函数
        e(i,j)  = exp(-((i-n1)^2+(j-n1)^2)/(4*eta))/(4*pi*eta);
    end
end
%卷积滤波
nr3 = imfilter(mr,e,'conv', 'replicate');
ng3 = imfilter(mg,e,'conv', 'replicate');
nb3 = imfilter(mb,e,'conv', 'replicate');
ur3 = log(nr3);
ug3 = log(ng3);
ub3 = log(nb3);
tr3 = log(mr);
tg3 = log(mg);
tb3 = log(mb);
yr3 = (tr3-ur3)/3;
yg3 = (tg3-ug3)/3;
yb3 = (tb3-ub3)/3;
dr = yr1+yr2+yr3;
dg = yg1+yg2+yg3;
db = yb1+yb2+yb3;
cr = im2uint8(dr);
cg = im2uint8(dg);
cb = im2uint8(db);
% 集成处理后的分量得到结果图像
In = cat(3, cr, cg, cb);
%结果显示
figure;
subplot(2, 2, 1); imshow(f); title('原图像', 'FontWeight', 'Bold');
subplot(2, 2, 2); imshow(In); title('处理后的图像', 'FontWeight', 'Bold');
% 灰度化，用于计算直方图
Q = rgb2gray(f);
M = rgb2gray(In);
subplot(2, 2, 3); imhist(Q, 64); title('原灰度直方图', 'FontWeight', 'Bold');
subplot(2, 2, 4); imhist(M, 64); title('处理后的灰度直方图', 'FontWeight', 'Bold');
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3 结果图示