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专业: 通信理论与系统

[交流] 【求助】帮忙看下这四个matlab算法，我是菜鸟已有2人参与

帮忙看看这四个算法的作用，最好能讲解和对比下，非常感谢

第一个grayWorld.m
function grayWorld(filename,outFile,catType,maxIter,plots)
%grayWorld(filename,outfile,catType,maxIter,plot)
% Performs color balancing via the gray world assumption and a chromatic
% adpatation transform.  Can be run interatively to possibly improve the
% result.  Set plot = 0 or 1 to turn diagnostic plots on or off.

tutorialinit

%% import image
im_orig = cbimread(filename);
% figure
% imshow(im_orig)
% title('Original Image')

%% various constants
xyz_D65 = [95.04; 100; 108.88]; %http://en.wikipedia.org/wiki/D65, normalized Y = 100

sRGBtoXYZ = [0.4124564  0.3575761  0.1804375; ...
            0.2126729  0.7151522  0.0721750; ...
            0.0193339  0.1191920  0.9503041];

%% grayworld
% catType = 'vonKries';
% maxIter = 1;
b = 0.001; %convergence threshold

imRGB_orig = cbreshape(im_orig)*255;
imRGB = imRGB_orig;

grayDiff =[];
for iter = 1:maxIter
rgbEst = mean(imRGB,2); %grayworld, average everything
grayDiff = [grayDiff norm([rgbEst(1)-rgbEst(2),rgbEst(1)-rgbEst(3),rgbEst(2)-rgbEst(3)])];

if grayDiff(end) < b
      disp(['Converged. RGB difference vector < ' num2str(b) ' in magnitude.'])
      break
elseif iter >= 2 && abs(grayDiff(end-1)-grayDiff(end)) < 10^-6
      disp(['RGB difference vector no longer improving.'])
      break
end

xyEst = XYZ2xy(sRGBtoXYZ*rgbEst); %calculate xy chromaticity
xyzEst = xy2XYZ(xyEst,100); %normalize Y to 100 so D65 luminance comparable
imRGB = cbCAT(xyzEst,xyz_D65,catType)*imRGB;
end

imwrite(cbunshape(imRGB,size(im_orig))/255,outFile,'png');

if plots
length(grayDiff) %number of iterations done
%    figure
%    imshow(cbunshape(imRGB,size(im_orig))/255)
%    title('Gray World Corrected')

figure
plot(grayDiff)
title('GW: Norm of RGB Difference Vector vs Iterations')
print(gcf,'-dpng',[outFile '-fig1'])
end

第二个simplestColorBalance.m
function simplestColorBalance(filename,outFile,satLevel,plots)
%simplestColorBalance(filename,outFile,satLevel,plot)
% Performs color balancing via histogram normalization.
% satLevel controls the percentage of pixels to clip to white and black.
% Set plot = 0 or 1 to turn diagnostic plots on or off.

tutorialinit

%% import image
im_orig = cbimread(filename);
% figure
% imshow(im_orig)
% title('Original Image')

%% full width histogram method
% satLevel = .01; %percentage of the image to saturate to black or white, tweakable param
q = [satLevel/2 1-satLevel/2];

imRGB_orig = cbreshape(im_orig)*255;
imRGB = zeros(size(imRGB_orig));
N = size(imRGB_orig,2);
color = {'r','g','b'};
for ch = 1:3
if plots
      figure
      subplot(211)
      hist(imRGB_orig(ch,:),256)
      set(findobj(gca,'Type','patch'),'FaceColor',color{ch},'EdgeColor',color{ch})
      xlim([0 255])
      title('Original Histogram')
end
tiles = quantile(imRGB_orig(ch,:),q);
%    [sum(imRGB_orig(ch,:)tiles(2))/N] %check percentages are correct
imRGB(ch,:) = cbsaturate(imRGB_orig(ch,:),tiles); %saturate at the appropriate pts. in distribution
bottom = min(imRGB(ch,:)); top = max(imRGB(ch,:));
imRGB(ch,:) = (imRGB(ch,:)-bottom)*255/(top-bottom);

if plots
      subplot(212)
      hist(imRGB(ch,:),256)
      set(findobj(gca,'Type','patch'),'FaceColor',color{ch},'EdgeColor',color{ch})
      xlim([0 255])
      title('Corrected Histogram')
end
print(gcf,'-dpng',[outFile '-fig' num2str(ch)])
end

imwrite(cbunshape(imRGB,size(im_orig))/255,outFile,'png');
% figure
% imshow(cbunshape(imRGB,size(im_orig))/255)
% title('Simplest Color Balance Corrected')

第三个robustAWB.m
function robustAWB(filename,outFile,option,catType,T,maxIter,plots)
%robustAWB(filename,outFile,option,catType,T,maxIter,plot)
% Performs robust auto white-balancing by estimating gray pixels based on
% their deviation in YUV space then applying an iterative correction via
% CAT or directly adjusting the R and B channels.
% Set option = 'RB gain' or 'cat' for the correction method.
% Set T higher for a larger threshold of deviation to consider off-gray.
% Set plot = 0 or 1 to turn diagnostic plots on or off.

tutorialinit

%% import image
im_orig = cbimread(filename);
% figure
% imshow(im_orig)
% title('Original Image')

%% various constants
xyz_D65 = [95.04; 100; 108.88]; %http://en.wikipedia.org/wiki/D65, normalized Y = 100

sRGBtoXYZ = [0.4124564  0.3575761  0.1804375; ...
            0.2126729  0.7151522  0.0721750; ...
            0.0193339  0.1191920  0.9503041];

%% robust auto white balance - YUV grays
% option = 'RB gain'; % adjustments are done as a gain to only R or B channelas descripted in Huo
% option = 'cat';
% catType = 'CAT02';

% maxIter = 1000;

% T = 0.3; %this should be tweakable slider
% T = 0.1;
% gain adjustment parameters, can probably be optimized via more control system analysis
u = .01; %gain step size
a = .8; %double step threshold
b = .001; %convergence threshold

% rgb to yuv
xfm = [0.299 0.587 0.144; ...
      -0.299 -0.587 0.886; ...
      0.701 -0.587 -0.114];

inv_xfm = inv(xfm);

imRGB_orig = cbreshape(im_orig)*255;
imRGB = imRGB_orig;
gain = [1 1 1];

U_avg = [];
V_avg = [];
totGray = [];
for iter = 1:maxIter
im = xfm*imRGB; %convert to YUV
% find gray chromaticity
% (|U|+|V|)/Y
F = ( abs(im(2,:)) + abs(im(3,:)) )./im(1,:);

%    figure
%    imshow(cbunshape(imRGB/255.*repmat(F %    % imshow(cbunshape(inv_xfm)*im+[F>T;zeros(2,length(F))],size(im_orig))) %rubylith overlays
%    title('Gray Pixels Found by Robust AWB'),xlabel('Pixels in their original color are considered gray')

totGray = [totGray sum(F if totGray(end) == 0
      disp('No valid gray pixels found.')
      break
end
grays = im(:,F U_bar = mean(grays(2,:));
V_bar = mean(grays(3,:));

U_avg = [U_avg U_bar];
V_avg = [V_avg V_bar];

if strcmpi(option,'cat')
      if max(abs([U_bar V_bar])) < b
         disp(['Converged. U_bar and V_bar < ' num2str(b) ' in magnitude.'])
         break
      elseif iter >= 2 && norm([U_avg(end)-U_avg(end-1) V_avg(end)-V_avg(end-1)]) < 10^-6
         disp(['U_bar and V_bar are no longer improving.'])
         break
      end
      rgbEst = inv_xfm*[100;U_bar;V_bar]; %convert the average gray from YUV to RGB
      xyEst = XYZ2xy(sRGBtoXYZ*rgbEst); %calculate xy chromaticity
      xyzEst = xy2XYZ(xyEst,100); %normalize Y to 100 so D65 luminance comparable
      imRGB = cbCAT(xyzEst,xyz_D65,catType)*imRGB;
else
      if abs(U_bar) > abs(V_bar) % U > V; blue needs adjustment
         err = U_bar;
         ch = 3; %blue channel
      else
         err = V_bar;
         ch = 1; %red channel
      end
      if abs(err) >= a
         delta = 2*sign(err)*u; %accelerate gain adjustment if far off
      elseif abs(err) < b %converged
         delta = 0;
         disp(['Converged. U_bar and V_bar < ' num2str(b) ' in magnitude.'])
         break
      else
         delta = err*u;
      end
%       [err,delta]
      gain(ch) = gain(ch)-delta; %negative fdbk loop
      imRGB = diag(gain)*imRGB_orig;
end
end

imwrite(cbunshape(imRGB,size(im_orig))/255,outFile,'png');
% figure
% imshow(cbunshape(imRGB,size(im_orig))/255)
% title('Robust AWB Corrected')
if plots
length(U_avg)
figure
plot(totGray)
title('rAWB: Total Gray Pixels vs Iterations')
print(gcf,'-dpng',[outFile '-fig1'])

figure
hold on
plot(U_avg,'b')
plot(V_avg,'r')
hold off
title('rAWB: Mean Chromaticity vs Iterations')
legend('U','V')
print(gcf,'-dpng',[outFile '-fig2'])

figure
imshow(cbunshape(imRGB/255.*repmat(F % imshow(cbunshape(inv_xfm)*im+[F>T;zeros(2,length(F))],size(im_orig))) %rubylith overlays
title('Gray Pixels Found by Robust AWB'),xlabel('Pixels in their original color are considered gray')
print(gcf,'-dpng',[outFile '-fig3'])
end

第四个sensorCorrelation.m
function sensorCorrelation(filename,outFile,catType,plots)
%sensorCorrelation(filename,outFile,catType,plot)
% Performs the sensor correlation method for illuminant estimation and then
% a chromatic adaptation transform for correction.
% Set plot = 0 or 1 to turn diagnostic plots on or off.

% clear
tutorialinit
load refGamut.mat %a sample set of reference gamuts simulated by ISET for mired = 118:23.5:400

xyz_D65 = [95.04; 100; 108.88]; %http://en.wikipedia.org/wiki/D65, normalized Y = 100
%% import image
im_orig = cbimread(filename);
% figure
% imshow(im_orig)
% title('Original Image')

%% sensor correlation
clipVal = 225;

imRGB_orig = cbreshape(im_orig)*255;
imRGB = imRGB_orig;

%% pre-process
imRGB = imRGB(:,max(imRGB,[],1)= 225+1 since we will saturate later

%populate color space
imRGBSpace = zeros(clipVal+1,clipVal+1,clipVal+1,'uint8');
for i=1:size(imRGB,2)
c = imRGB(:,i);
imRGBSpace(c(1)+1,c(2)+1,c(3)+1) = imRGBSpace(c(1)+1,c(2)+1,c(3)+1) || 1;
end

%compute connectivity lookup table
% imRGBSpaceConn = imfilter(imRGBSpace,conndef(size(imRGB,1),'maximal')).*imRGBSpace>2; %only keep colors that are connected to more than 2 neighbors
%the above is the truly correct way but since we'll never access the color
%entries not in the image, we don't need to zero them out, this is good enough and faster
imRGBSpaceConn = imfilter(imRGBSpace,conndef(size(imRGB,1),'maximal'))>2; %must have more than 1 neighbor

imRGBConn = false(1,size(imRGB,2));
for i=1:length(imRGBConn)
c = imRGB(:,i);
imRGBConn(i) = imRGBSpaceConn(c(1)+1,c(2)+1,c(3)+1);
end
imRGB = imRGB(:,imRGBConn); %keep only pixels in the image with connected color values
clear imRGBSpaceConn imRGBSpace imRGBConn

imRGB = imRGB(:,max(imRGB,[],1)= 225, saturated

% normalize
I = zeros(1,size(imRGB_orig,2));
for i = 1:length(I)
I(i) = norm(imRGB_orig(:,i)); %sqrt(R^2+G^2+B^2)
end
imRGB = 255*imRGB/max(I);

%%
[gamut_im,gamutArea_im] = convhull(imRGB(1,:),imRGB(3,:)); %image gamut is the convex hull of (R,B) points
[x,y] = poly2cw(imRGB(1,gamut_im), imRGB(3,gamut_im));
gamut_im = [x; y]; %the image gamut vertices

% gamut_illum =[]; %gamuts of typical illuminants %2xnx# illum
% gamutArea_illum = []; % # illum x 1

kScale = .1:.1:1;
corr = zeros(length(kScale),length(gamutArea_illum)); %rows are scaling, cols are different illuminants
for j=1:length(kScale)
k = kScale(j);
for i = 1:length(gamut_temps)
      gi = gamut_illum{i};
      [x,y] = polybool('&',k*gamut_im(1,:),k*gamut_im(2,:),gi(1,:),gi(2,:));
      corr(j,i) = polyarea(x,y)/sqrt(gamutArea_im*gamutArea_illum(i));
end
end
% corr
[corr,k] = max(corr,[],1);
[maxCorrVal,targetIllum] = max(corr);
k = k(targetIllum);
T = gamut_temps(targetIllum)

%% correct
xyzEst = xy2XYZ(XYZ2xy(gamut_XYZ(:,targetIllum)),100); %xyz color cast estimate normalized Y to 100

% catType = 'vonKries';

imRGB = imRGB_orig;
imRGB = cbCAT(xyzEst,xyz_D65,catType)*imRGB;

% figure
% imshow(cbunshape(imRGB,size(im_orig))/255)
% title(['Sensor Correlation Corrected, T=' num2str(T)])

imwrite(cbunshape(imRGB,size(im_orig))/255,outFile,'png');
%% plot
if plots
figure,clf
title('Gamut Plots')
for i=1:length(gamut_temps)
      gi = gamut_illum{i};
      x = gi(1,:);
      y = gi(2,:);
      hold on
      if i == targetIllum
         patch(x, y, 1, 'FaceColor', 'b')
      else
         patch(x, y, 1, 'FaceColor', 'g')
      end
      hold off
end
hold on
% plot(imRGB(1,:),imRGB(3,:),'.')
patch(gamut_im(1,:), gamut_im(2,:), 1, 'FaceColor', 'r')
hold off
print(gcf,'-dpng',[outFile '-fig1'])
end
% [k2,a2] = convhull(x2,y2);
%
% [xp1,yp1] = poly2cw(x1(k1),y1(k1));
% [xp2,yp2] = poly2cw(x2(k2),y2(k2));
% % xp1 = x1(k1); yp1 = y1(k1);
% % xp2 = x2(k2); yp2 = y2(k2);
%
%
% [x,y] = polybool('&',xp1,yp1,xp2,yp2);
%
% % figure(10),clf
% % hold on
% % patch(x, y, 1, 'FaceColor', 'g')
% % plot(x1,y1,'.',x2,y2,'r.')
% % plot(x1(k1),y1(k1),'-',x2(k2),y2(k2),'r-')
% % hold off
%
% corr = polyarea(x,y)/sqrt(a1*a2)
end

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