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ISP模块之色彩增强算法--HSV空间Saturation通道调整 .

时间:2016-08-12 19:50:04      阅读:782      评论:0      收藏:0      [点我收藏+]

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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

    技术分享

    HSV的圆锥模型

    技术分享

    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。

 

  1. % Color Enhancement  
  2. clc,clear,close all  
  3. src1 = imread(‘src.bmp‘);  
  4. src2 = imread(‘src_40.bmp‘);  
  5.   
  6. src1_hsv = rgb2hsv(src1);  
  7. src2_hsv = rgb2hsv(src2);  
  8.   
  9. h1 = src1_hsv(:,:,1);  
  10. s1 = src1_hsv(:,:,2);  
  11. v1 = src1_hsv(:,:,3);  
  12.   
  13. h2 = src2_hsv(:,:,1);  
  14. s2 = src2_hsv(:,:,2);  
  15. v2 = src2_hsv(:,:,3);  
  16. %   
  17. meanS1 = mean(s1(:));  
  18. varS1  = std2(s1);   
  19. %   
  20. meanS2 = mean(s2(:));  
  21. varS2  = std2(s2);   
  22. %   
  23. deltaS = s2 - s1;  
  24. deltaV = v2 - v1;  
  25.   
  26. %% test1 : 观测“原饱和度-饱和度调整增量”的关系 saturation and delta saturation  
  27. figure;  
  28. oriS = zeros(101,2);  
  29. s3 = s1;  
  30. j = 1;  
  31. for i = 0: 0.01 : 1  
  32.     oriS(j,1) = i + 0.01;  
  33.     oriS(j,2) =  mean(deltaS(find(s1 > i & s1< i + 0.01)));  
  34.     j = j + 1;  
  35. end  
  36. X  = oriS(:,1);  
  37. Y  = oriS(:,2);  
  38. XX = oriS(:,1) * 255;  
  39. YY = oriS(:,2) * 255;  
  40. 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

 

  1. %% Color Enhancement Module -- Authored by HuangDao,08/17/2015  
  2. % functions: input a image of type BMP or PNG, the program will decide to  
  3. % do the Color Enhancement choice for you.There are four types of Enhanced  
  4. % intensity - 20,40,60,80.The larger number stands for stronger  
  5. % enhancement.  
  6. % And we can also choose the simple color channel(eg.R,G,B) to do the  
  7. % enhancement.There are also four different types of enhanced intensity.  
  8. %  
  9. % parameters table  
  10. %  ------------------------------------------------------------------------  
  11. % | Enhanced  |     MATLAB params             |      OpenCV params         |  
  12. % | intensity |p1        p2        p3         | p1        p2        p3     |  
  13. % | 20        |-0.1661   0.2639    -0.003626  |-0.0006512 0.2639    -0.9246|  
  14. % | 40        |-0.4025   0.6238    -0.0005937 |0.001578   0.6238    -0.1514|  
  15. % | 60        |1.332     1.473     -0.01155   |-0.005222  1.473     -2.946 |  
  16. % | 80        |-4.813    3.459     -0.004568  |-0.01887   3.459     -1.165 |  
  17. %  ------------------------------------------------------------------------  
  18.   
  19. clc; clear ;close all  
  20. % 载入文件夹  
  21. pathName = ‘.\‘;  
  22. fileType = ‘*.bmp‘;  
  23. files    = dir([pathName fileType]);  
  24. len      = length(files);  
  25.   
  26. for pic = 5%1:1:len  
  27.     srcName = files(pic).name;  
  28.     srcImg  = imread(srcName);  
  29.     srcHSV  = rgb2hsv(srcImg);  
  30.     srcH    = srcHSV(:,:,1);  
  31.     srcS    = srcHSV(:,:,2);  
  32.     srcV    = srcHSV(:,:,3);  
  33.     meanS   = mean(srcS(:));  
  34.     varS    = std2(srcS);  
  35.     %图像整体进行色彩增强处理  
  36.     if (meanS >= 0.5)  
  37.         p1 = 0;p2 = 0;p3 = 0;  
  38.     else if (meanS >= 0.35 && meanS 0.5)  
  39.             p1 = -0.1661;p2 = 0.2639;p3 = -0.003626;  
  40.         else if (meanS >=0.2 && meanS <0.35)  
  41.                 p1 = -0.4025;p2 = 0.6238;p3 = -0.0005937;  
  42.             else  
  43.                 p1 = 1.332;p2 = 1.473;p3 = -0.01155;  
  44.             end  
  45.         end  
  46.     end  
  47.     dstS = srcS + p1*srcS.*srcS + p2*srcS + p3 ;  
  48.     dstHSV = srcHSV;  
  49.     dstHSV(:,:,2) = dstS;  
  50.     dstImg = hsv2rgb(dstHSV);  
  51.     figure;imshow(srcImg);  
  52.     figure;imshow(dstImg);  
  53.     %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])  
  54.     p11 = -0.4025;p21 = 0.6238;p31 = -0.0005937;%周边杂色调整系数,40  
  55.     p12 = 1.332;  p22 = 1.473; p32 = -0.01155;  %纯色区域调整系数,60  
  56.     compHue = srcH;  
  57.     GcompS  = dstS;  
  58.     RcompS  = dstS;  
  59.     BcompS  = dstS;  
  60.     channel = ‘B‘;  
  61.     switch channel  
  62.         case ‘G‘  
  63.             I1 = find(compHue > 0.2083 & compHue <0.2917);  
  64.             GcompS(I1) = dstS(I1) + dstS(I1).*dstS(I1)*p11 + dstS(I1)*p21 + p31;  
  65.             I2 = find(compHue >= 0.2917 & compHue <= 0.3750);  
  66.             GcompS(I2) = dstS(I2) + dstS(I2).*dstS(I2)*p12 + dstS(I2)*p22 + p32;  
  67.             I3 = find(compHue > 0.3750 & compHue <0.4583);  
  68.             GcompS(I3) = dstS(I3) + dstS(I3).*dstS(I3)*p11 + dstS(I3)*p21 + p31;  
  69.             compHSV = dstHSV;  
  70.             compHSV(:,:,2) = GcompS;  
  71.             dstImgG = hsv2rgb(compHSV);  
  72.             figure;imshow(dstImgG);  
  73.         case ‘R‘  
  74.             I1 = find(compHue > 0.875 & compHue <0.9583);  
  75.             RcompS(I1) = dstS(I1) + dstS(I1).*dstS(I1)*p11 + dstS(I1)*p21 + p31;  
  76.             I2 = find(compHue >= 0.9583 | compHue <= 0.0417);  
  77.             RcompS(I2) = dstS(I2) + dstS(I2).*dstS(I2)*p12 + dstS(I2)*p22 + p32;  
  78.             I3 = find(compHue > 0.0417 & compHue <0.125);  
  79.             RcompS(I3) = dstS(I3) + dstS(I3).*dstS(I3)*p11 + dstS(I3)*p21 + p31;  
  80.             compHSV = dstHSV;  
  81.             compHSV(:,:,2) = RcompS;  
  82.             dstImgR = hsv2rgb(compHSV);  
  83.             figure;imshow(dstImgR);  
  84.         case ‘B‘  
  85.             I1 = find(compHue > 0.5417 & compHue <0.625);  
  86.             BcompS(I1) = dstS(I1) + dstS(I1).*dstS(I1)*p11 + dstS(I1)*p21 + p31;  
  87.             I2 = find(compHue >= 0.625 & compHue <= 0.7083);  
  88.             BcompS(I2) = dstS(I2) + dstS(I2).*dstS(I2)*p12 + dstS(I2)*p22 + p32;  
  89.             I3 = find(compHue > 0.7083 & compHue <0.7917);  
  90.             BcompS(I3) = dstS(I3) + dstS(I3).*dstS(I3)*p11 + dstS(I3)*p21 + p31;  
  91.             compHSV = dstHSV;  
  92.             compHSV(:,:,2) = BcompS;  
  93.             dstImgB = hsv2rgb(compHSV);  
  94.             figure;imshow(dstImgB);  
  95.     end  
  96.     %进行R,G,B通道之间的互换  
  97.     convH = zeros(size(srcH,1),size(srcH,2)); %convert  
  98.     deltaHue = 240;  
  99.     switch deltaHue  
  100.         case 120  
  101.             disp(‘R -> G‘)  
  102.             convH = srcH + 1/3;  
  103.             convH(find(convH >= 1)) = convH(find(convH >= 1)) - 1;  
  104.         case 240  
  105.             disp(‘R -> B‘)  
  106.             convH = srcH + 2/3;  
  107.             convH(find(convH >= 1)) = convH(find(convH >= 1)) - 1;  
  108.     end  
  109.     convHSV = dstHSV;  
  110.     convHSV(:,:,1) = convH;  
  111.     convImg = hsv2rgb(convHSV);  
  112.     figure;imshow(convImg)  
  113.     pause();  
  114. 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:

 

  1. Mat srcHSV,sat,satAdj,dstMerge,dst;     //sat - saturation饱和度分量  
  2. Mat imageAwb = imread("m_ImageAwb.bmp");  
  3. vector<Mat> channels,channels1;  
  4. double p1,p2,p3;  
  5.   
  6. cvtColor(imageAwb,srcHSV,CV_BGR2HSV);  
  7. split(srcHSV,channels);  
  8. split(srcHSV,channels1);  
  9. sat = channels.at(1);  
  10. Scalar m = mean(sat);  
  11.   
  12. if (m(0) <= 51.5)                         
  13. {p1 = -0.002714 , p2 = 0.9498, p3 = -0.5073;  AfxMessageBox("High Color Enhancement!"); }//高  
  14. else if (m(0) > 38.5 && m(0) <= 89.5)    
  15. {p1 = -0.001578  , p2 = 0.6238, p3 = -0.1514;AfxMessageBox("Middle Color Enhancement!"); }//中  
  16. else if (m(0) > 89.5 && m(0) <=127.5)    
  17. {p1 = -0.0006512, p2 = 0.2639, p3 = -0.9246;AfxMessageBox("Low Color Enhancement!");}//低  
  18. else                                     
  19. {p1 = 0,p2 = 0,p3 =0;AfxMessageBox("No Color Enhancement!");}  
  20.   
  21. satAdj = sat;  
  22. for (int i = 0 ; i < sat.rows;i ++)  
  23. {  
  24.     for (int j = 0;j < sat.cols;j ++)  
  25.     {  
  26.         uchar val = sat.at<uchar>(i,j);  
  27.         satAdj.at<uchar>(i,j) = (val + p1 * val * val + p2 * val + p3) ;  
  28.     }  
  29. }  
  30.   
  31. channels1.at(1) = satAdj;  
  32. merge(channels1,dstMerge);  
  33. cvtColor(dstMerge,dst,CV_HSV2BGR);  
  34. 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);

 

   最后给出算法效果图:

Group1.原图->增强后

技术分享技术分享

 

Group2.原图->R通道增强->颜色通道改变R2B

技术分享技术分享技术分享

Group3.原图->增强后->颜色通道改变R2B

技术分享技术分享技术分享

完!下篇讲Local Tone Mapping。

ISP模块之色彩增强算法--HSV空间Saturation通道调整 .

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原文地址:http://www.cnblogs.com/whw19818/p/5765999.html

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