MATLAB: Finding the mean of a group of images

filterimageimage processingmean

Hello everyone,

So I am trying to find the mean of a group of images so I can compare their mean before and after the filter.

for l = 1:numel(bmpFiles)
    %applying the filter
    
    bflt_img1{l} = bfilter2(img1{l},w,sigma);
    
    %finding the average of the images
    I0 = imread(img1{l});
    sumImage = double (I0)
    grayImage = imread(img1{l+1});
    sumImage = sumImage + double(grayImage);
    meanImage = sumImage / numel(bmpFiles);
    
end

However, I get this error message:

Error using imread>parse_inputs (line 504)
The file name or URL argument must be a character vector or string scalar.
Error in imread (line 342)
[source, fmt_s, extraArgs, was_cached_fmt_used] = parse_inputs(cached_fmt, varargin{:});
Error in bil1_MH (line 21)
    I0 = imread(img1{l});

Is there a problem with using img1{L} as a format to refer to the image?

Thanks in advance.

Best Answer

bfilter2() is not part of MATLAB, so we do not know what it does. There is a File Exchange contribution https://www.mathworks.com/matlabcentral/fileexchange/12191-bilateral-filtering but it would give an error if img1{l} were not floating point. Either you are using a different bfilter2() or else img1{l} is floating point.

If img1{l} is floating point, that would explain the error message when you tried to imread() it. If you have already read the data into img1 then just

I0 = img1{l};

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Here's my demo. It calculates local mean and standard deviation. I leave it up to you to figure out how to enhance the image once you have those images (there are a variety of ways). Someday perhaps I'll upload all my demos to the File Exchange in a "grab bag of image processing demos" but until then here it is:

% Demo to take the local mean, variance, and standard deviation
% of a gray scale image.
% userImage, if passed in, is used as the image.
% If userImage is not passed in, user is asked to use a demo image.
% Code written by ImageAnalyst
function local_variance(userImage)
% Clean up.
clc;    % Clear the command window.
close all;  % Close all figures (except those of imtool.)
workspace;  % Make sure the workspace panel is showing.
% Change the current folder to the folder of this m-file.
% (The line of code below is from Brett Shoelson of The Mathworks.)
% Don't use these lines if you're calling this from another m-file.
if(~isdeployed)
  cd(fileparts(which(mfilename)));
end
% Check that user has the Image Processing Toolbox installed.
hasIPT = license('test', 'image_toolbox');
if ~hasIPT
  % User does not have the toolbox installed.
  message = sprintf('Sorry, but you do not seem to have the Image Processing Toolbox.\nDo you want to try to continue anyway?');
  reply = questdlg(message, 'Toolbox missing', 'Yes', 'No', 'Yes');
  if strcmpi(reply, 'No')
    % User said No, so exit.
    return;
  end
end
% Initialize.
fontSize = 20;
if nargin == 0
    % No image passed in on the command line.
    % Read in one of the standard MATLAB demo images
    % as our original gray scale image and display it.
    promptMessage = sprintf('Which image do you want to use.\nThe coins or the cameraman?');
    button = questdlg(promptMessage, 'Select Image', 'Coins', 'Cameraman', 'Coins');
    if strcmp(button, 'Coins')
        grayImage = double(imread('coins.png')); % Cast to double.


    else
        grayImage = double(imread('cameraman.tif')); % Cast to double.
    end
else
    % Use the image array passed in on the command line.
    grayImage = double(userImage); % Cast to double.
end
% Start timing.
startTime = tic;   
subplot(2, 3, 1);
imshow(grayImage, []);
title('Original Image', 'FontSize', fontSize);
set(gcf, 'Position', get(0,'Screensize')); % Maximize figure.
% Blur the image with a 5 by 5 averaging (box filter) window.
blurredImage = conv2(grayImage, ones(5,5)/25);
subplot(2, 3, 2);
imshow(blurredImage, []);
title('Blurred (Local Mean) Image', 'FontSize', fontSize);
% Perform a variance filter.
% Output image is the variance of the input image in a 3 by 3 sliding window.
VarianceFilterFunction = @(x) var(x(:));
varianceImage = nlfilter(grayImage, [3 3], VarianceFilterFunction);
% An alternate way of doing the variance filter is on the next line:
% varianceImage = reshape(std(im2col(originalImage,[3 3],'sliding')), size(originalImage)-2); 
subplot(2, 3, 3);
imshow(varianceImage, [])
title('Variance Image', 'FontSize', fontSize);
% Compute the square root of the variance image to get the standard deviation.
standardDeviationImage = sqrt(varianceImage);
subplot(2, 3, 4);
imshow(standardDeviationImage, [])
title('Standard Deviation Image', 'FontSize', fontSize);
% Compute the standard deviation filter with MATLAB's built-in stdfilt() filter.
standardDeviationImage2 = stdfilt(grayImage);
subplot(2, 3, 5);
imshow(standardDeviationImage2, [])
title('Built-in stdfilt() filter', 'FontSize', fontSize);
% Perform Sobel filter
% h  = fspecial('sobel') returns a 3-by-3 filter h (shown below) that emphasizes horizontal edges 
% using the smoothing effect by approximating a vertical gradient. 
% If you need to emphasize vertical edges, transpose the filter h'.
% [ 1  2  1
%   0  0  0
%  -1 -2 -1 ]
verticalSobelKernel = fspecial('sobel');
sobelImage = imfilter(grayImage, verticalSobelKernel);
subplot(2, 3, 6);
imshow(sobelImage, [])
title('Sobel edge filter', 'FontSize', fontSize);
elapsedTime = toc(startTime);
message = sprintf('Done!\n\nElapsed time = %.2f seconds.', elapsedTime);
msgbox(message);
return; % End of local_variance() function.

Best Answer

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