MATLAB: How to correct inhomogeneous intensity in image

background correctioncorrectign inhomogeneous intensityflat fieldImage Processing Toolbox

Dear,

I am new to MATLAB, i need some help from you guys.The below image has inhomogeneous intensity distribution, the intensity is max at the top and it is decreasing linearly as it is going down.

Idea was to consider the small portion of the images top corner and bottom corner and to loop through those pixels, get the intensity difference and set it to the max intensity value.
for example if the intensity is getting reduced in step of 5 after reaching center of the image than see the difference and correct it by adding that difference

Thank you in advance

The image:

Best Answer

I = im2double(imread('blob.bmp'));
y = mean(I(:, 1:100), 2);
sigma = 30; % choosen by visual inspection
G = fspecial('gaussian', 3*sigma+1, sigma);
yb = imfilter(y, G, 'replicate');
Ic = bsxfun(@plus, I, yb(1) - yb);
subplot(1,2,1), imshow(I)
subplot(1,2,2), imshow(Ic)

Related Solutions

MATLAB: Counting the number of muscle fibres in an image

Vishakha:

You might have to take just one color channel, then get rid of the local intensity variations with a bottom hat filter. Then threshold and call bwareafilt() to get rid of blobs bigger or smaller than the known size of the fibers. Then call bwlabel() to count the number of fibers.

Try this for a start - adapt as needed:

clc;    % Clear the command window.
close all;  % Close all figures (except those of imtool.)
clear;  % Erase all existing variables. Or clearvars if you want.
workspace;  % Make sure the workspace panel is showing.
format short g;
format compact;
fontSize = 25;
%===============================================================================

% Get the name of the image the user wants to use.
baseFileName = 'netmuscle.jpg';
% Get the full filename, with path prepended.
folder = []; % Determine where demo folder is (works with all versions).
fullFileName = fullfile(folder, baseFileName);
%===============================================================================
% Read in a demo image.
grayImage = imread(fullFileName);
% Get the dimensions of the image.
% numberOfColorChannels should be = 1 for a gray scale image, and 3 for an RGB color image.
[rows, columns, numberOfColorChannels] = size(grayImage)
if numberOfColorChannels > 1
  % It's not really gray scale like we expected - it's color.
  % Use weighted sum of ALL channels to create a gray scale image.
%   grayImage = rgb2gray(grayImage);
  % ALTERNATE METHOD: Convert it to gray scale by taking only the green channel,
  % which in a typical snapshot will be the least noisy channel.
  grayImage = grayImage(:, :, 3); % Take blue channel.
end
% Display the image.


subplot(1, 2, 1);
imshow(grayImage, []);
axis on;
axis image;
caption = sprintf('Original Gray Scale Image');
title(caption, 'FontSize', fontSize, 'Interpreter', 'None');
drawnow;
hp = impixelinfo();
% Set up figure properties:
% Enlarge figure to full screen.
set(gcf, 'Units', 'Normalized', 'OuterPosition', [0, 0, 1, 1]);
% Get rid of tool bar and pulldown menus that are along top of figure.
% set(gcf, 'Toolbar', 'none', 'Menu', 'none');
% Give a name to the title bar.
set(gcf, 'Name', 'Demo by ImageAnalyst', 'NumberTitle', 'Off')
drawnow;
% Do a bottom hat filter to find the small dark spots.
subplot(1, 2, 2);
histogram(grayImage);
grid on;
se = strel('disk', 15);
filteredImage = imbothat(grayImage, se);
% Display the image.
imshow(filteredImage, []);
histogram(filteredImage);
grid on;
% Binarize the image.
% binaryImage = imbinarize(grayImage); 
binaryImage = filteredImage >35;  % Dtermine number from histogram.
% Make sure there are blobs only between 100 and 800 pixels.
binaryImage = bwareafilt(binaryImage, [100, 800]);
% Display the image.
subplot(1, 2, 2);
imshow(binaryImage, []);
axis on;
axis image;
caption = sprintf('Binary Image');
title(caption, 'FontSize', fontSize, 'Interpreter', 'None');
drawnow;
hp = impixelinfo();
% Label the image
[labeledImage, numBlobs] = bwlabel(binaryImage);
% Make measurements of bounding box
props = regionprops(labeledImage, 'Area');
allAreas = [props.Area]
sortedAread = sort(allAreas)
caption = sprintf('Binary Image with %d Muscle Fibers', numBlobs);
title(caption, 'FontSize', fontSize, 'Interpreter', 'None');

MATLAB: Binary Image – Black region detection

Mohsina, try this:

clc;    % Clear the command window.
close all;  % Close all figures (except those of imtool.)
clear;  % Erase all existing variables. Or clearvars if you want.
workspace;  % Make sure the workspace panel is showing.
format short g;
format compact;
fontSize = 25;
%===============================================================================

% Get the name of the image the user wants to use.
baseFileName = 'image1.png';
% Get the full filename, with path prepended.
folder = []; % Determine where demo folder is (works with all versions).
fullFileName = fullfile(folder, baseFileName);
%===============================================================================
% Read in a demo image.
grayImage = imread(fullFileName);
% Get the dimensions of the image.
% numberOfColorChannels should be = 1 for a gray scale image, and 3 for an RGB color image.
[rows, columns, numberOfColorChannels] = size(grayImage)
if numberOfColorChannels > 1
  % It's not really gray scale like we expected - it's color.
  % Use weighted sum of ALL channels to create a gray scale image.
  grayImage = rgb2gray(grayImage);
  % ALTERNATE METHOD: Convert it to gray scale by taking only the green channel,
  % which in a typical snapshot will be the least noisy channel.
  % grayImage = grayImage(:, :, 2); % Take green channel.
end
% Display the image.



subplot(2, 2, 1);
imshow(grayImage, []);
axis on;
axis image;
caption = sprintf('Original Gray Scale Image');
title(caption, 'FontSize', fontSize, 'Interpreter', 'None');
drawnow;
hp = impixelinfo();
% Set up figure properties:
% Enlarge figure to full screen.
set(gcf, 'Units', 'Normalized', 'OuterPosition', [0, 0, 1, 1]);
% Get rid of tool bar and pulldown menus that are along top of figure.
% set(gcf, 'Toolbar', 'none', 'Menu', 'none');
% Give a name to the title bar.
set(gcf, 'Name', 'Demo by ImageAnalyst', 'NumberTitle', 'Off')
drawnow;
% Create binary image of the black blobs from grayscale image.
binaryImage = grayImage == 0;
% Get rid of the background.
binaryImage = imclearborder(binaryImage);
% Display the image.
subplot(2, 2, 2);
imshow(binaryImage, []);
axis on;
axis image;
title('Binary Image', 'FontSize', fontSize, 'Interpreter', 'None');
% Label the blobs
[labeledImage, numBlobs] = bwlabel(binaryImage);
% Display the image.
subplot(2, 2, 2);
imshow(binaryImage, []);
axis on;
axis image;
title('Binary Image', 'FontSize', fontSize, 'Interpreter', 'None');
% Let's assign each blob a different color to visually show the user the distinct blobs.
coloredLabels = label2rgb (labeledImage, 'hsv', 'k', 'shuffle'); % pseudo random color labels
% coloredLabels is an RGB image.  We could have applied a colormap instead (but only with R2014b and later)
subplot(2, 2, 3);
imshow(coloredLabels);
axis on;
axis image; % Make sure image is not artificially stretched because of screen's aspect ratio.
caption = sprintf('Pseudo colored labels, from label2rgb().');
title(caption, 'FontSize', fontSize);
drawnow;
% Make measurements of bounding box.
props = regionprops(labeledImage, 'BoundingBox');
% Display the image.
subplot(2, 2, 4);
imshow(binaryImage, []);
axis on;
axis image;
title('Binary Image with Boxes', 'FontSize', fontSize, 'Interpreter', 'None');
% Display the bounding boxes around them.
hold on;
for k = 1 : numBlobs
  thisBoundingBox = props(k).BoundingBox
  rectangle('Position', thisBoundingBox, ...
    'EdgeColor', 'c', 'LineWidth', 2);
end

Best Answer

Related Solutions

MATLAB: Counting the number of muscle fibres in an image

MATLAB: Binary Image – Black region detection

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