## why the G channel value of a pixel is different in different positions?

### satish thapaliya (view profile)

on 10 Dec 2012

I took an image of the object and estimated its g channel value at some points. Next time i changed the position of the object and took the image again, the g channel value of the same pixel was not same in the new position. what could be the reason? the position of the image in spatial cordinates was (293,250) in first case and it was changed to (301,250) in the second case. the command that i used is as follow

```a=0.85;
I1=I(:,:,2);
I2=I(:,:,3);
G_channel=mean(mean(I1(293+(-5:5),250+(-5:5))))-mean(mean(I2(293+(-5:5),250+(-5:5))))*a;
```

command used in the second case

```a=0.85;
I1=I(:,:,2);
I2=I(:,:,3);
G_channel=mean(mean(I1(310+(-5:5),250+(-5:5))))-mean(mean(I2(310+(-5:5),250+(-5:5))))*a;
```

But the results shows a wide difference???

Image Analyst

### Image Analyst (view profile)

on 11 Dec 2012

Did you do any background correction to take into account lens shading (vignetting)? That can have a major impact. It can also correct for nonuniform illumination. I'm guessing you didn't divide by a white background shot, right?

satish thapaliya

### satish thapaliya (view profile)

on 12 Dec 2012

I didn't do any background correction for vignetting. i also didn't divide by a white background shot. If this is the cause of error how it could be edited?

Image Analyst

### Image Analyst (view profile)

on 12 Dec 2012

Take an image of a blank white or gray background. Smooth it out or fit it to a model like a 2D polynomial. Divide each channel by the max. So basically you now have an RGB image where each pixel if the percent of the max for that channel. For example, in the corner you may find that R is 89% of the middle (where the max Red is), that G is 91% and B is 90%. So now to compensate for that and "flat field" your image you need to divide by that percentage image. That will make it so that no matter where your object is in your field of view, it will have the same intensity. Look down in my answer for a demo of it for a gray scale image. Basically do the same thing for each color channel of a color image.

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### Image Analyst (view profile)

on 10 Dec 2012

Why do you think the green mean should be the same everywhere? If I just go outside and take a photo there is no reason to expect that the green mean is the same everywhere. Please upload an image to explain why.

```310+(-5:5)
```

which is confusing and not very MATLAB-like, do it like this:

```305:315
```

which is easily understood that you want to go from row 305 up through row 315.

### Image Analyst (view profile)

on 12 Dec 2012

Demo to flat field a gray scale image:

```% demo to background correct a MATLAB demo image.
% Estimates the background from the image itself.
% Then models it to a 4th order polynomial in both directions
% using John D'Errico's File Exchange submission:
% http://www.mathworks.com/matlabcentral/fileexchange/34765-polyfitn
function BGCorrect_demo()
try
clc;    % Clear the command window.
close all;  % Close all figures (except those of imtool.)
imtool close all;  % Close all imtool figures.
clear;  % Erase all existing variables.
workspace;  % Make sure the workspace panel is showing.
fontSize = 16;

% Read in the noisy nonuniform background image.
% Read in a standard MATLAB gray scale demo image.
folder = fullfile(matlabroot, '\toolbox\images\imdemos');
baseFileName = 'AT3_1m4_01.tif';
baseFileName = 'rice.png';
% Get the full filename, with path prepended.
fullFileName = fullfile(folder, baseFileName);
% Check if file exists.
if ~exist(fullFileName, 'file')
% File doesn't exist -- didn't find it there.  Check the search path for it.
fullFileName = baseFileName; % No path this time.
if ~exist(fullFileName, 'file')
% Still didn't find it.  Alert user.
errorMessage = sprintf('Error: %s does not exist in the search path folders.', fullFileName);
uiwait(warndlg(errorMessage));
return;
end
end
[rows columns numberOfColorChannels] = size(grayImage)
% Display the original background image.
subplot(2, 3, 1);
imshow(grayImage);  % Display image.
title('Original Image', 'FontSize', fontSize);
```
```	% Enlarge figure to full screen.
set(gcf, 'Position', get(0,'Screensize'));
drawnow; % Force display to update immediately.
```
```	% Let's compute and display the histogram.
[pixelCount grayLevels] = imhist(grayImage);
subplot(2, 3, 2);
bar(pixelCount);
grid on;
title('Histogram of original image', 'FontSize', fontSize);
xlim([0 grayLevels(end)]); % Scale x axis manually.
hPixelInfo = impixelinfo();

% Let's process this to get the background, which will be the smooth areas.
filteredImage = entropyfilt(grayImage, ones(5));
subplot(2, 3, 2);
imshow(filteredImage, []);  % Display image.
title('Entropy-Filtered Image', 'FontSize', fontSize);
binaryImage = filteredImage > 5;
binaryImage = imfill(binaryImage, 'holes');
subplot(2, 3, 3);
imshow(binaryImage, []);  % Display image.
caption = sprintf('Binary Image\nThe white blobs will be excluded\nfrom the background modeling.');
title(caption, 'FontSize', fontSize);
% Fill in the textured areas with local background
% to get an estimate of what the background
% would be without the objects in there.
nonuniformBackgroundImage = roifill(grayImage, binaryImage);
subplot(2, 3, 4);
imshow(nonuniformBackgroundImage, []);  % Display image.
caption = sprintf('Non-Uniform Background Image\ncreated with roifill.\nUsed for training the model.');
title(caption, 'FontSize', fontSize);

% Now we have an otiginal gray scale image and a background image.
% If you're able to take a "blank shot" - an image with no objects
% in there in the first place - that would be better than trying
% to estimate a background from an image with objects in it.
% Anyway, we now have our images and we're ready to begin!
```
```	% Get the modeled background image.
% Fit our actual background with a 2D polynomial.
modeledBackgroundImage = GetModelBackgroundImage(nonuniformBackgroundImage);
% Scale the model for display so that it looks more like the original.
meanOfOriginal = mean(nonuniformBackgroundImage(:))
meanOfModel = mean(modeledBackgroundImage(:))
modelImageForDisplay = modeledBackgroundImage * meanOfOriginal / meanOfModel;
meanOfDisplayModel = mean(modelImageForDisplay(:))

% Display the model background image.
subplot(2, 3, 5);
modelImageForDisplay = uint8(modelImageForDisplay);
imshow(modelImageForDisplay, []);  % Display image.
title('Model Background', 'FontSize', fontSize);

% Do background correction on the raw background image.
correctedBackgroundImage = BackgroundCorrect(nonuniformBackgroundImage, modeledBackgroundImage);
subplot(2, 3, 6);
cla reset;
imshow(correctedBackgroundImage);  % Display image.
caption = sprintf('Corrected Background Image\n(just to see how well it corrects itself).');
title(caption, 'FontSize', fontSize);
```
```	% Bring up a new figure to show some more image.
figure;
% Enlarge figure to full screen.
set(gcf, 'Position', get(0,'Screensize'));
drawnow; % Force display to update immediately.
% Show the original images, the background image and the actual image.
subplot(2, 3, 1);
imshow(modelImageForDisplay, []);  % Display image.
title('Model Background Image.', 'FontSize', fontSize);
subplot(2, 3, 2);
imshow(nonuniformBackgroundImage, []);  % Display image.
title('Original Background Image.', 'FontSize', fontSize);
subplot(2, 3, 3);
imshow(grayImage, []);  % Display image.
title('Original Gray Scale Image.', 'FontSize', fontSize);

% Show the corrected background image.
subplot(2, 3, 5);
imshow(correctedBackgroundImage);  % Display image.
caption = sprintf('Corrected background image,\ncorrected using the model.');
title(caption, 'FontSize', fontSize);

% Do background correction on the original gray scale image.
correctedGrayImage = BackgroundCorrect(grayImage, modeledBackgroundImage);
subplot(2, 3, 6);
imshow(correctedGrayImage, []);  % Display image.
caption = sprintf('Corrected Gray Scale image,\ncorrected using the model.');
title(caption, 'FontSize', fontSize);
```
```	message = sprintf('Done with demo!\nBe sure to check out both figure windows.');
msgbox(message);
catch ME
errorMessage = sprintf('Error in function BGCorrect_demo().\n\nError Message:\n%s', ME.message);
fprintf(1, '%s\n', errorMessage);
uiwait(warndlg(errorMessage));
end
return; % from BGCorrect_demo
```
```%================================================================================================================
% Divides the test image by the modeled image to get a background corrected image.
function correctedImage = BackgroundCorrect(inputImage, modeledBackgroundImage)
try
correctedImage = inputImage;
[rows columns numberOfColorChannels] = size(inputImage);
if numberOfColorChannels > 1
% It's a color image.  Correct each color channel one at a time.
for colorIndex = 1 : 3
oneColorChannel = inputImage(:, :, colorIndex);
% maxNoisyValue = max(max(oneColorChannel));
```
```			% Model it to get rid of noise, scratches, smudges, etc.
noiselessBackgroundImage = modeledBackgroundImage(:, :, colorIndex);
```
```			% Divide the actual image by the modeled image.
% 		maxValue = max(max(noiselessBackgroundImage));
% 		correctedColorChannel = uint8(maxValue * single(oneColorChannel) ./ noiselessBackgroundImage);
correctedColorChannel = uint8(single(oneColorChannel) ./ noiselessBackgroundImage);
correctedImage(:, :, colorIndex) = correctedColorChannel;
end
else
% It's actually a gray scale image.
% Divide the actual image by the modeled image.
% 		maxValue = max(max(noiselessBackgroundImage));
correctedImage = uint8(single(inputImage) ./ modeledBackgroundImage);
end
catch ME
errorMessage = sprintf('Error in function BackgroundCorrect().\n\nError Message:\n%s', ME.message);
fprintf(1, '%s\n', errorMessage);
uiwait(warndlg(errorMessage));
end
return; % from BackgroundCorrect
```
```%================================================================================================================
% Takes a color, noisy, non-uniform background image and fits a 2D model to it.
% Returns a noise-free, smooth color image where each color plane is independently
% normalized with a max value of 1.  See lengthy comment farther down.
% modeledBackgroundImage is a double image in the range 0-1.
function modeledBackgroundImage = GetModelBackgroundImage(nonuniformBackgroundImage)
try
% Get the dimensions of the image.  numberOfColorBands should be = 3.
% [rows columns numberOfColorBands] = size(nonuniformBackgroundImage);
```
```	% Preallocate output image.
modeledBackgroundImage = zeros(size(nonuniformBackgroundImage));
[rows columns numberOfColorChannels] = size(nonuniformBackgroundImage);
if numberOfColorChannels > 1
% It's a color image.  Correct each color channel one at a time.
for colorIndex = 1 : 3
oneColorChannel = nonuniformBackgroundImage(:, :, colorIndex);
```
```			% Model it to get rid of noise, scratches, smudges, etc.
noiselessImage = ModelOneColorChannel(oneColorChannel);
% Divide the actual image by the modeled image.
maxValue = max(max(noiselessImage));
% Divide by the max of the modeled image so that you can get a "percentage" image
% that you can later divide by to get the color corrected image.
% Note: each color channel may have a different max value,
% but you do not want to divide by the max of all three color channel
% because if you do that you'll also be making all color channels have the same
% range, in effect "whitening" the image in addition to correcting for non-uniform
% background intensity.  This is not what you want to do.
% You want to correct for background non-uniformity WITHOUT changing the color.
% That can be done as a separate step if desired.
noiselessImage = noiselessImage / maxValue; % This is a percentage image.
```
```			% Now insert that percentage image for this one color channel into the color percentage image.
modeledBackgroundImage(:, :, colorIndex) = noiselessImage;
```
```		% 	minValue = min(modeledBackgroundImage(:))
% 	maxValue = max(modeledBackgroundImage(:))
end
else
% It's a gray scale image.  (Much simpler situation.)
% Model it to get rid of noise, scratches, smudges, etc.
noiselessImage = ModelOneColorChannel(nonuniformBackgroundImage);
% Divide the actual image by the modeled image.
maxValue = max(max(noiselessImage));
% Divide by the max of the modeled image so that you can get a "percentage" image
% that you can later divide by to get the color corrected image.
modeledBackgroundImage = noiselessImage / maxValue; % This is a percentage image.
```
```		% 	minValue = min(modeledBackgroundImage(:))
% 	maxValue = max(modeledBackgroundImage(:))
end
```
```	catch ME
errorMessage = sprintf('Error in function GetSmoothedBackgroundImage().\n\nError Message:\n%s', ME.message);
fprintf(1, '%s\n', errorMessage);
uiwait(warndlg(errorMessage));
end
% Free up memory.  Should be automatically freed but we have had problems here.
% (Make sure you don't clear any input or output arguments or you'll get an exception.)
clear('noiselessImage', 'oneColorChannel');
return; % from GetSmoothedBackgroundImage

%================================================================================================================
% Fit a biquartic model to the image.
% You need to have a path to polyfitn(), John D'Errico's File Exchange submission.
function modeledColorChannel = ModelOneColorChannel(colorChannel)
try
modeledColorChannel = colorChannel; % Initialize.
rows = size(colorChannel, 1);
columns = size(colorChannel, 2);
% 		midX = columns / 2;
% 		midY = rows / 2;
[X,Y] = meshgrid(1:columns, 1:rows);
z = colorChannel;
x1d = reshape(X, numel(X), 1);
y1d = reshape(Y, numel(Y), 1);
z1d = double(reshape(z, numel(z), 1));
x = [x1d y1d];

%--------------------------------------------------------
% Get a 4th order polynomial model.
% CHANGE THE ORDER HERE IF YOU WANT TO.
%--------------------------------------------------------
polynomialOrder = 4;
p = polyfitn(x, z1d, polynomialOrder);
```
```		% Evaluate on a grid and plot:
zg = polyvaln(p, x);
modeledColorChannel = reshape(zg, [rows columns]);

catch ME
errorMessage = sprintf('Error in ModelOneColorChannel():\n%s', ME.message);
if strfind(errorMessage, 'HELP MEMORY')
memory;
errorMessage = sprintf('%s\n\nCheck console for memory information.', errorMessage);
end
uiwait(warndlg(errorMessage));
end
% Free up memory.  Should be automatically freed but we have had problems here.
% (Make sure you don't clear any input or output arguments or you'll get an exception.)
clear('zg', 'x1d', 'y1d', 'z1d', 'X', 'Y', 'x', 'z', 'p');
return; % from ModelOneColorChannel
```

Image Analyst

### Image Analyst (view profile)

on 12 Dec 2012

Sorry - I don't have any simpler code all ready to hand over to you. Basically you need to divide your "real" images by a blank image (the image of a uniform scene). Essentially it's like this:

```correctedImage = actualImage ./ percentageImage;
```

where percentage image is the "blank" image divided by the max value in each color channel.

satish thapaliya

### satish thapaliya (view profile)

on 13 Dec 2012

Ok. I think i have used the concept but there is still some problem for me to use the code as following. could you comment on it I=imread('3.BMP'); %actual image >> i=imread('bckgrd.bmp'); %blank white background image >> for colorindex=1:3 colorchannel=I(:,:,colorindex); maxvalue=max(max(colorchannel)); end >> PI=i./maxvalue; %percentage image >> correctedimage=I./PI; ??? Array dimensions must match for binary array op.

Image Analyst

### Image Analyst (view profile)

on 13 Dec 2012

i is different that I. Also, your maxvalue is just the maxvalue of the last (blue) channel. You can't do that or else you'll be doing white balancing at the same time, not just background correction. You need to have maxvalue(colorindex) so that you have 3 maxes - one for each color channel. Then divide each blank color channel by it's own max. Then divide your image by the percentage image - you can do that all in one line as a vectorized statement you don't need to have a loop over color channels dividing each channel at a time. Also, i and I are not good variable names it's hard to tell the difference between them (i & I) and 1 and l. Call it rgbImage or originalImage or something similarly descriptive. Besides, i is the imaginary constant sqrt(-1) and you could run into confusion with that if you every use complex numbers. But the main problem seems to be that your background image i is not the same size as the I image 3.bmp. Use whos to find the sizes. Then say why they are not the same size.

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