non-uniform illumination - Scenes are changing!

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thelabmaster 2021 年 11 月 22 日
コメント済み: thelabmaster 2021 年 11 月 25 日
I am trying to calculate/analyze coefficient of variance (CV) for multiple series of images and compare them at the end. Please see the code below.
The foreground (various shades of green that is fluorescent fluid) is separated from the background (dark blue) via Otsu. All the pixels that fall below the threshold are assigned with 0 intensity. The intensity of those that are higher than the threshold, is preserved. mean intensity is then calculated, around which the standard deviation (STD) is calculated. The STD is then divided by the mean intensity of the image. This process repeats for each image in a series (15 images). The area coverage and pattern of the images are different from one another. I have attached a full series analysis where CV, histogram, mean intensity, threshold value, and the image is presented. I have also attached a few images from another series so that the difference in illumination becomes clearer.
However, the illumination is non-uniform ( in both vertical and horizontal directions) within each series and from image to image. This causes irregularities in CV measurement. I have looked at "imhistmatch", white balance correction, gray level correction, homomorphic filters, and median filters. Honestly, I cannot think of any other method. None of them works because the scenes are changing from image to image. I truly appreciate any feedback/guidance on the matter.
P.s: No background-only or foreground-only image was taken. All images have both foreground and background.
fileList = dir('*.jpg');
fileList = {};
thr = [];
im = imread(fileList{1});
img = im(:,:,2);
refSize = size(img);
CV = [];
num_of_soil_pixels = [];
num_of_foam_pixels = [];
mean_collect = [];
thr_collect = [];
sum_foam_intensities = [];
std_only =[];
%%%%% Defining circular areas over which calcs occur %%%%%%%%%%%%%
diam = mean(refSize);
%%%%% Iteration process to compute and store CV values for each image %%%%%
for i = 1:length(fileList)
im = imread(fileList{i});
% Resize images with respect to the first image
if size(im(:,:,2))~= refSize
im_resized = imresize(im, refSize);
im_resized = im;
% Convert to grayscale
img = rgb2gray(im_resized);
% Threshold the image using Otsu
thr = graythresh(img);
segmented_img = imbinarize(img, thr);
out_img = double(img) .* segmented_img;
% Normalize the image via dividing by the range
out_img = 255*(out_img - min(min(out_img)))/(max(max(out_img))-min(min(out_img)));
% Create a circular mask to obtain the region of interest (circle)
mask = zeros(refSize);
[xgrid, ygrid] = meshgrid(1:refSize(2), 1:refSize(1));
mask = ((xgrid-(refSize(1)/2)).^2 + (ygrid-(refSize(1)/2)).^2) <= (diam/2.2).^2;
% compare thresholded image with the RGB version - validate how well
% Otsu detects the green regions (foreground)
%fh2 = figure(23);
%fh2.WindowState = 'maximized';
% mask the grayscale image
values = out_img(mask);
% Calculate the mean
mymean = mean(values);
% store various variables for plotting purposes
mean_collect = [mean_collect;mymean];
thr_collect = [thr_collect;thr*255];
CV = [stdev; std(values)/mymean];
std_only = [std_only;std(values)];
num_of_soil_pixels = [num_of_soil_pixels;sum(values==0)];
num_of_foam_pixels = [num_of_foam_pixels;nnz(values)];
sum_foam_intensities =[sum_foam_intensities;sum(values(values~=0))];
figure (1)
plot(CV, 'ok','MarkerfaceColor','g')
grid on


Image Analyst
Image Analyst 2021 年 11 月 24 日
@thelabmaster, to get the mean and standard deviation only in the masked region, try this:
% Demo by Image Analyst, November 24, 2021.
clc; % Clear the command window.
close all; % Close all figures (except those of imtool.)
workspace; % Make sure the workspace panel is showing.
format long g;
format compact;
fontSize = 16;
fprintf('Beginning to run %s.m ...\n', mfilename);
% Read in image.
folder = [];
baseFileName = '03.jpg';
fullFileName = fullfile(folder, baseFileName);
% Check if file exists.
if ~isfile(fullFileName)
% The file doesn't exist -- didn't find it there in that folder.
% Check the entire search path (other folders) for the file by stripping off the folder.
fullFileNameOnSearchPath = baseFileName; % No path this time.
if ~exist(fullFileNameOnSearchPath, 'file')
% Still didn't find it. Alert user.
errorMessage = sprintf('Error: %s does not exist in the search path folders.', fullFileName);
fullFileName = fullFileNameOnSearchPath;
rgbImage = imread(fullFileName);
[rows, columns, numberOfColorChannels] = size(rgbImage)
% Display the image.
subplot(2, 2, 1);
imshow(rgbImage, []);
axis('on', 'image');
caption = sprintf('Original Image : "%s"', baseFileName);
title(caption, 'FontSize', fontSize, 'Interpreter', 'None');
hp = impixelinfo(); % Set up status line to see values when you mouse over the image.
% Set up figure properties:
% Enlarge figure to full screen.
hFig1 = gcf;
hFig1.Units = 'Normalized';
hFig1.WindowState = 'maximized';
% 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.
hFig1.Name = 'Demo by Image Analyst';
% Segment (mask) the image.
[mask, maskedRGBImage] = createMask(rgbImage);
% Display the mask image.
subplot(2, 2, 2);
imshow(mask, []);
axis('on', 'image');
caption = sprintf('Mask Image');
title(caption, 'FontSize', fontSize, 'Interpreter', 'None');
hp = impixelinfo(); % Set up status line to see values when you mouse over the image.
% Display the masked image.
subplot(2, 2, 3);
axis('on', 'image');
caption = sprintf('Masked Image');
title(caption, 'FontSize', fontSize, 'Interpreter', 'None');
hp = impixelinfo(); % Set up status line to see values when you mouse over the image.
% Compute intensity image
grayImage = rgb2gray(maskedRGBImage);
% Compute mean in the mask region.
meanGL = mean(grayImage(mask))
% Compute standard deviation in the mask region.
stdGL = std(double(grayImage(mask)))
function [BW,maskedRGBImage] = createMask(RGB)
%createMask Threshold RGB image using auto-generated code from colorThresholder app.
% [BW,MASKEDRGBIMAGE] = createMask(RGB) thresholds image RGB using
% auto-generated code from the colorThresholder app. The colorspace and
% range for each channel of the colorspace were set within the app. The
% segmentation mask is returned in BW, and a composite of the mask and
% original RGB images is returned in maskedRGBImage.
% Auto-generated by colorThresholder app on 24-Nov-2021
% Convert RGB image to chosen color space
I = rgb2hsv(RGB);
% Define thresholds for channel 1 based on histogram settings
channel1Min = 0.247;
channel1Max = 0.565;
% Define thresholds for channel 2 based on histogram settings
channel2Min = 0.272;
channel2Max = 1.000;
% Define thresholds for channel 3 based on histogram settings
channel3Min = 0.236;
channel3Max = 1.000;
% Create mask based on chosen histogram thresholds
sliderBW = (I(:,:,1) >= channel1Min ) & (I(:,:,1) <= channel1Max) & ...
(I(:,:,2) >= channel2Min ) & (I(:,:,2) <= channel2Max) & ...
(I(:,:,3) >= channel3Min ) & (I(:,:,3) <= channel3Max);
BW = sliderBW;
% Initialize output masked image based on input image.
maskedRGBImage = RGB;
% Set background pixels where BW is false to zero.
maskedRGBImage(repmat(~BW,[1 1 3])) = 0;
You'll see in the command window:
meanGL =
stdGL =
  7 件のコメント
thelabmaster 2021 年 11 月 25 日
Understood. However, the experiments are done a while ago and no more photos can be taken at this point.


その他の回答 (1 件)

yanqi liu
yanqi liu 2021 年 11 月 23 日
sir,may be do some image registration to register images
  2 件のコメント
yanqi liu
yanqi liu 2021 年 11 月 24 日
yes,sir,may be use sift or surf points to make image match


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