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2018-10-19
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摘要：本文主要向大家介绍了Photoshop入门学习之ISP模块之色彩增强算法--HSV空间Saturation通道调整，通过具体的内容向大家展现，希望对大家Photoshop入门学习有所帮助。
本文主要向大家介绍了Photoshop入门学习之ISP模块之色彩增强算法--HSV空间Saturation通道调整，通过具体的内容向大家展现，希望对大家Photoshop入门学习有所帮助。
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色彩增强不同于彩色图像增强，图像增强的一般处理方式为直方图均衡化等，目的是为了增强图像局部以及整体对比度。而色彩增强的目的是为了使的原有的不饱和的色彩信息变得饱和、丰富起来。对应于Photoshop里面的“色相/饱和度”调节选项里面对饱和度的操作。色彩增强的过程，并不改变原有彩色图像的颜色以及亮度信息。
在我的色彩增强算法模块里面，始终只针对色彩饱和度(Saturation)信息做研究，调整。这样的话，那就不得不介绍HSV颜色空间了，H代表Hue（色彩），S代表Saturation（饱和度），V代表Value，也可用B表示（Brightness，明度），HSV空间也可称作HSB空间。
HSV空间在wikipedia上的介绍，https://en.wikipedia.org/wiki/HSL_and_HSV
下面根据自己的理解介绍一下HSV空间，以及其各通道在Matlab和OpenCV中的不同。
HSV的圆柱模型
HSV的圆锥模型
从上图可以看出，在HSV空间中，Hue通道的取值从0-360°变化时，颜色从红->黄->绿->青->蓝逐步变化。Saturation从0->1变化时，色彩逐渐加深变成纯色（pure）。Value值从0->1变化时，图像整体亮度增加，V值为0时，图像为全黑，V值为1时，图像为全白。
Matlab RGB色彩空间向HSV转换，采用函数rgb2hsv,转换后的hsv各通道的元素取值范围为[0,1];OpenCV中彩色图像向HSV空间中转换，cvtColor(src,srcHsv,CV_BGR2HSV),转换后H的取值范围为[0,180],S,V的取值范围为[0,255].
下面介绍自己的算法处理思路，后面会给出完整的Matlab代码：
步骤一、给出一张原图src，用PS进行饱和度(Saturation)+40处理后另存为src_40;
步骤二、将以上两张图像分别转换到hsv空间，提取出饱和度信息，分别为S，S_40;
步骤三、统计饱和度增加40后，原色彩饱和度与饱和度增量之间的对应关系，即S -- (S_40-S);
步骤四、对关系S -- (S_40-S)进行二次多项式曲线拟合，得到二次曲线f(x) = p1*x^2 + p2*x + p3；
为什么是二次？1.对应关系呈现出抛物线形状；2.更高次曲线并没有明显改善拟合性能，且计算消耗会变高。
步骤五、任意给定输出图像input，根据其色彩饱和度信息，即可进行色彩增强40处理，新的饱和度信息可以表示为S‘(x) = S(x) + f(x),得到增强后的色彩信息后返回RGB图像输出；
步骤六、分别对原图+20，+40，+60后进行饱和度信息统计，并得到相应拟合参数，设置为色彩增强的低、中、高三挡，在实际处理过程中，根据输入图像input自身色彩饱和度信息(均值)自适应选取相应参数进行色彩增强；
步骤七、按需对某一单独颜色通道进行色彩增强处理，例如绿色范围为105°-135°，在对该范围进行增强的同时，还需对75°-105°,135°-165°进行一半强度的增强，这样才会保证色彩的连续性，不会出现色斑；
步骤八、按需对色彩(Hue)进行转换；
代码部分：第一部分用作估计拟合参数，在Curve fitting tool里面对X，Y进行拟合，得到曲线参数。
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% Color Enhancement
clc,clear,close all
src1 = imread(‘src.bmp‘);
src2 = imread(‘src_40.bmp‘);
src1_hsv = rgb2hsv(src1);
src2_hsv = rgb2hsv(src2);
h1 = src1_hsv(:,:,1);
s1 = src1_hsv(:,:,2);
v1 = src1_hsv(:,:,3);
h2 = src2_hsv(:,:,1);
s2 = src2_hsv(:,:,2);
v2 = src2_hsv(:,:,3);
%
meanS1 = mean(s1(:));
varS1 = std2(s1);
%
meanS2 = mean(s2(:));
varS2 = std2(s2);
%
deltaS = s2 - s1;
deltaV = v2 - v1;
%% test1 : 观测“原饱和度-饱和度调整增量”的关系 saturation and delta saturation
figure;
oriS = zeros(101,2);
s3 = s1;
j = 1;
for i = 0: 0.01 : 1
oriS(j,1) = i + 0.01;
oriS(j,2) = mean(deltaS(find(s1 > i & s1< i + 0.01)));
j = j + 1;
end
X = oriS(:,1);
Y = oriS(:,2);
XX = oriS(:,1) * 255;
YY = oriS(:,2) * 255;
plot(XX,YY)
% Color Enhancement
clc,clear,close all
src1 = imread(‘src.bmp‘);
src2 = imread(‘src_40.bmp‘);
src1_hsv = rgb2hsv(src1);
src2_hsv = rgb2hsv(src2);
h1 = src1_hsv(:,:,1);
s1 = src1_hsv(:,:,2);
v1 = src1_hsv(:,:,3);
h2 = src2_hsv(:,:,1);
s2 = src2_hsv(:,:,2);
v2 = src2_hsv(:,:,3);
%
meanS1 = mean(s1(:));
varS1 = std2(s1);
%
meanS2 = mean(s2(:));
varS2 = std2(s2);
%
deltaS = s2 - s1;
deltaV = v2 - v1;
%% test1 : 观测“原饱和度-饱和度调整增量”的关系 saturation and delta saturation
figure;
oriS = zeros(101,2);
s3 = s1;
j = 1;
for i = 0: 0.01 : 1
oriS(j,1) = i + 0.01;
oriS(j,2) = mean(deltaS(find(s1 > i & s1< i + 0.01)));
j = j + 1;
end
X = oriS(:,1);
Y = oriS(:,2);
XX = oriS(:,1) * 255;
YY = oriS(:,2) * 255;
plot(XX,YY)
第二部分，对输入图像进行高、中、低三级自适应增强处理
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%% Color Enhancement Module -- Authored by HuangDao,08/17/2015
% functions: input a image of type BMP or PNG, the program will decide to
% do the Color Enhancement choice for you.There are four types of Enhanced
% intensity - 20,40,60,80.The larger number stands for stronger
% enhancement.
% And we can also choose the simple color channel(eg.R,G,B) to do the
% enhancement.There are also four different types of enhanced intensity.
%
% parameters table
% ------------------------------------------------------------------------
% | Enhanced | MATLAB params | OpenCV params |
% | intensity |p1 p2 p3 | p1 p2 p3 |
% | 20 |-0.1661 0.2639 -0.003626 |-0.0006512 0.2639 -0.9246|
% | 40 |-0.4025 0.6238 -0.0005937 |0.001578 0.6238 -0.1514|
% | 60 |1.332 1.473 -0.01155 |-0.005222 1.473 -2.946 |
% | 80 |-4.813 3.459 -0.004568 |-0.01887 3.459 -1.165 |
% ------------------------------------------------------------------------
clc; clear ;close all
% 载入文件夹
pathName = ‘.\‘;
fileType = ‘*.bmp‘;
files = dir([pathName fileType]);
len = length(files);
for pic = 5%1:1:len
srcName = files(pic).name;
srcImg = imread(srcName);
srcHSV = rgb2hsv(srcImg);
srcH = srcHSV(:,:,1);
srcS = srcHSV(:,:,2);
srcV = srcHSV(:,:,3);
meanS = mean(srcS(:));
varS = std2(srcS);
%图像整体进行色彩增强处理
if (meanS >= 0.5)
p1 = 0;p2 = 0;p3 = 0;
else if (meanS >= 0.35 && meanS < 0.5)
p1 = -0.1661;p2 = 0.2639;p3 = -0.003626;
else if (meanS >=0.2 && meanS <0.35)
p1 = -0.4025;p2 = 0.6238;p3 = -0.0005937;
else
p1 = 1.332;p2 = 1.473;p3 = -0.01155;
end
end
end
dstS = srcS + p1*srcS.*srcS + p2*srcS + p3 ;
dstHSV = srcHSV;
dstHSV(:,:,2) = dstS;
dstImg = hsv2rgb(dstHSV);
figure;imshow(srcImg);
figure;imshow(dstImg);
%指定R,G,B通道进行色彩增强处理,红色范围（[225-255]）,绿色范围（75-[105-135]-165）,蓝色范围（[-15-15]）
p11 = -0.4025;p21 = 0.6238;p31 = -0.0005937;%周边杂色调整系数，40
p12 = 1.332; p22 = 1.473; p32 = -0.01155; %纯色区域调整系数，60
compHue = srcH;
GcompS = dstS;
RcompS = dstS;
BcompS = dstS;
channel = ‘B‘;
switch channel
case ‘G‘
I1 = find(compHue > 0.2083 & compHue <0.2917);
GcompS(I1) = dstS(I1) + dstS(I1).*dstS(I1)*p11 + dstS(I1)*p21 + p31;
I2 = find(compHue >= 0.2917 & compHue <= 0.3750);
GcompS(I2) = dstS(I2) + dstS(I2).*dstS(I2)*p12 + dstS(I2)*p22 + p32;
I3 = find(compHue > 0.3750 & compHue <0.4583);
GcompS(I3) = dstS(I3) + dstS(I3).*dstS(I3)*p11 + dstS(I3)*p21 + p31;
compHSV = dstHSV;
compHSV(:,:,2) = GcompS;
dstImgG = hsv2rgb(compHSV);
figure;imshow(dstImgG);
case ‘R‘
I1 = find(compHue > 0.875 & compHue <0.9583);
RcompS(I1) = dstS(I1) + dstS(I1).*dstS(I1)*p11 + dstS(I1)*p21 + p31;
I2 = find(compHue >= 0.9583 | compHue <= 0.0417);
RcompS(I2) = dstS(I2) + dstS(I2).*dstS(I2)*p12 + dstS(I2)*p22 + p32;
I3 = find(compHue > 0.0417 & compHue <0.125);
RcompS(I3) = dstS(I3) + dstS(I3).*dstS(I3)*p11 + dstS(I3)*p21 + p31;
compHSV = dstHSV;
compHSV(:,:,2) = RcompS;
dstImgR = hsv2rgb(compHSV);
figure;imshow(dstImgR);
case ‘B‘
I1 = find(compHue > 0.5417 & compHue <0.625);
BcompS(I1) = dstS(I1) + dstS(I1).*dstS(I1)*p11 + dstS(I1)*p21 + p31;
I2 = find(compHue >= 0.625 & compHue <= 0.7083);
BcompS(I2) = dstS(I2) + dstS(I2).*dstS(I2)*p12 + dstS(I2)*p22 + p32;
I3 = find(compHue > 0.7083 & compHue <0.7917);
BcompS(I3) = dstS(I3) + dstS(I3).*dstS(I3)*p11 + dstS(I3)*p21 + p31;
compHSV = dstHSV;
compHSV(:,:,2) = BcompS;
dstImgB = hsv2rgb(compHSV);
figure;imshow(dstImgB);
end
%进行R,G,B通道之间的互换
convH = zeros(size(srcH,1),size(srcH,2)); %convert
deltaHue = 240;
switch deltaHue
case 120
disp(‘R -> G‘)
convH = srcH + 1/3;
convH(find(convH >= 1)) = convH(find(convH >= 1)) - 1;
case 240
disp(‘R -> B‘)
convH = srcH + 2/3;
convH(find(convH >= 1)) = convH(find(convH >= 1)) - 1;
end
convHSV = dstHSV;
convHSV(:,:,1) = convH;
convImg = hsv2rgb(convHSV);
figure;imshow(convImg)
pause();
end
%% Color Enhancement Module -- Authored by HuangDao,08/17/2015
% functions: input a image of type BMP or PNG, the program will decide to
% do the Color Enhancement choice for you.There are four types of Enhanced
% intensity - 20,40,60,80.The larger number stands for stronger
% enhancement.
% And we can also choose the simple color channel(eg.R,G,B) to do the
% enhancement.There are also four different types of enhanced intensity.
%
% parameters table
% ------------------------------------------------------------------------
% | Enhanced | MATLAB params | OpenCV params |
% | intensity |p1 p2 p3 | p1 p2 p3 |
% | 20 |-0.1661 0.2639 -0.003626 |-0.0006512 0.2639 -0.9246|
% | 40 |-0.4025 0.6238 -0.0005937 |0.001578 0.6238 -0.1514|
% | 60 |1.332 1.473 -0.01155 |-0.005222 1.473 -2.946 |
% | 80 |-4.813 3.459 -0.004568 |-0.01887 3.459 -1.165 |
% ------------------------------------------------------------------------
clc; clear ;close all
% 载入文件夹
pathName = ‘.\‘;
fileType = ‘*.bmp‘;
files = dir([pathName fileType]);
len = length(files);
for pic = 5%1:1:len
srcName = files(pic).name;
srcImg = imread(srcName);
srcHSV = rgb2hsv(srcImg);
srcH = srcHSV(:,:,1);
srcS = srcHSV(:,:,2);
srcV = srcHSV(:,:,3);
meanS = mean(srcS(:));
varS = std2(srcS);
%图像整体进行色彩增强处理
if (meanS >= 0.5)
p1 = 0;p2 = 0;p3 = 0;
else if (meanS >= 0.35 && meanS < 0.5)
p1 = -0.1661;p2 = 0.2639;p3 = -0.003626;
else if (meanS >=0.2 && meanS <0.35)
p1 = -0.4025;p2 = 0.6238;p3 = -0.0005937;
else
p1 = 1.332;p2 = 1.473;p3 = -0.01155;
end
end
end
dstS = srcS + p1*srcS.*srcS + p2*srcS + p3 ;
dstHSV = srcHSV;
dstHSV(:,:,2) = dstS;
dstImg = hsv2rgb(dstHSV);
figure;imshow(srcImg);
figure;imshow(dstImg);
%指定R,G,B通道进行色彩增强处理,红色范围（[225-255]）,绿色范围（75-[105-135]-165）,蓝色范围（[-15-15]）
p11 = -0.4025;p21 = 0.6238;p31 = -0.0005937;%周边杂色调整系数，40
p12 = 1.332; p22 = 1.473; p32 = -0.01155; %纯色区域调整系数，60
compHue = srcH;
GcompS = dstS;
RcompS = dstS;
BcompS = dstS;
channel = ‘B‘;
switch channel
case ‘G‘
I1 = find(compHue > 0.2083 & compHue <0.2917);
GcompS(I1) = dstS(I1) + dstS(I1).*dstS(I1)*p11 + dstS(I1)*p21 + p31;
I2 = find(compHue >= 0.2917 & compHue <= 0.3750);
GcompS(I2) = dstS(I2) + dstS(I2).*dstS(I2)*p12 + dstS(I2)*p22 + p32;
I3 = find(compHue > 0.3750 & compHue <0.4583);
GcompS(I3) = dstS(I3) + dstS(I3).*dstS(I3)*p11 + dstS(I3)*p21 + p31;
compHSV = dstHSV;
compHSV(:,:,2) = GcompS;
dstImgG = hsv2rgb(compHSV);
figure;imshow(dstImgG);
case ‘R‘
I1 = find(compHue > 0.875 & compHue <0.9583);
RcompS(I1) = dstS(I1) + dstS(I1).*dstS(I1)*p11 + dstS(I1)*p21 + p31;
I2 = find(compHue >= 0.9583 | compHue <= 0.0417);
RcompS(I2) = dstS(I2) + dstS(I2).*dstS(I2)*p12 + dstS(I2)*p22 + p32;
I3 = find(compHue > 0.0417 & compHue <0.125);
RcompS(I3) = dstS(I3) + dstS(I3).*dstS(I3)*p11 + dstS(I3)*p21 + p31;
compHSV = dstHSV;
compHSV(:,:,2) = RcompS;
dstImgR = hsv2rgb(compHSV);
figure;imshow(dstImgR);
case ‘B‘
I1 = find(compHue > 0.5417 & compHue <0.625);
BcompS(I1) = dstS(I1) + dstS(I1).*dstS(I1)*p11 + dstS(I1)*p21 + p31;
I2 = find(compHue >= 0.625 & compHue <= 0.7083);
BcompS(I2) = dstS(I2) + dstS(I2).*dstS(I2)*p12 + dstS(I2)*p22 + p32;
I3 = find(compHue > 0.7083 & compHue <0.7917);
BcompS(I3) = dstS(I3) + dstS(I3).*dstS(I3)*p11 + dstS(I3)*p21 + p31;
compHSV = dstHSV;
compHSV(:,:,2) = BcompS;
dstImgB = hsv2rgb(compHSV);
figure;imshow(dstImgB);
end
%进行R,G,B通道之间的互换
convH = zeros(size(srcH,1),size(srcH,2)); %convert
deltaHue = 240;
switch deltaHue
case 120
disp(‘R -> G‘)
convH = srcH + 1/3;
convH(find(convH >= 1)) = convH(find(convH >= 1)) - 1;
case 240
disp(‘R -> B‘)
convH = srcH + 2/3;
convH(find(convH >= 1)) = convH(find(convH >= 1)) - 1;
end
convHSV = dstHSV;
convHSV(:,:,1) = convH;
convImg = hsv2rgb(convHSV);
figure;imshow(convImg)
pause();
end
添加OpenCV代码段：
[cpp] view plain copy print?
Mat srcHSV,sat,satAdj,dstMerge,dst; //sat - saturation饱和度分量
Mat imageAwb = imread("m_ImageAwb.bmp");
vector<Mat> channels,channels1;
double p1,p2,p3;
cvtColor(imageAwb,srcHSV,CV_BGR2HSV);
split(srcHSV,channels);
split(srcHSV,channels1);
sat = channels.at(1);
Scalar m = mean(sat);
if (m(0) <= 51.5)
{p1 = -0.002714 , p2 = 0.9498, p3 = -0.5073; AfxMessageBox("High Color Enhancement!"); }//高
else if (m(0) > 38.5 && m(0) <= 89.5)
{p1 = -0.001578 , p2 = 0.6238, p3 = -0.1514;AfxMessageBox("Middle Color Enhancement!"); }//中
else if (m(0) > 89.5 && m(0) <=127.5)
{p1 = -0.0006512, p2 = 0.2639, p3 = -0.9246;AfxMessageBox("Low Color Enhancement!");}//低
else
{p1 = 0,p2 = 0,p3 =0;AfxMessageBox("No Color Enhancement!");}
satAdj = sat;
for (int i = 0 ; i < sat.rows;i ++)
{
for (int j = 0;j < sat.cols;j ++)
{
uchar val = sat.at<uchar>(i,j);
satAdj.at<uchar>(i,j) = (val + p1 * val * val + p2 * val + p3) ;
}
}
channels1.at(1) = satAdj;
merge(channels1,dstMerge);
cvtColor(dstMerge,dst,CV_HSV2BGR);
imwrite("m_ImageCE.bmp",dst);
Mat srcHSV,sat,satAdj,dstMerge,dst; //sat - saturation饱和度分量
Mat imageAwb = imread("m_ImageAwb.bmp");
vector<Mat> channels,channels1;
double p1,p2,p3;
cvtColor(imageAwb,srcHSV,CV_BGR2HSV);
split(srcHSV,channels);
split(srcHSV,channels1);
sat = channels.at(1);
Scalar m = mean(sat);
if (m(0) <= 51.5)
{p1 = -0.002714 , p2 = 0.9498, p3 = -0.5073; AfxMessageBox("High Color Enhancement!"); }//高
else if (m(0) > 38.5 && m(0) <= 89.5)
{p1 = -0.001578 , p2 = 0.6238, p3 = -0.1514;AfxMessageBox("Middle Color Enhancement!"); }//中
else if (m(0) > 89.5 && m(0) <=127.5)
{p1 = -0.0006512, p2 = 0.2639, p3 = -0.9246;AfxMessageBox("Low Color Enhancement!");}//低
else
{p1 = 0,p2 = 0,p3 =0;AfxMessageBox("No Color Enhancement!");}
satAdj = sat;
for (int i = 0 ; i < sat.rows;i ++)
{
for (int j = 0;j < sat.cols;j ++)
{
uchar val = sat.at<uchar>(i,j);
satAdj.at<uchar>(i,j) = (val + p1 * val * val + p2 * val + p3) ;
}
}
channels1.at(1) = satAdj;
merge(channels1,dstMerge);
cvtColor(dstMerge,dst,CV_HSV2BGR);
imwrite("m_ImageCE.bmp",dst);
本文由职坐标整理并发布，希望对同学们有所帮助。了解更多详情请关注常用软件Photoshop频道！
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