MATLAB: Surface roughness and gaussian filtering

image analysisimage processingImage Processing Toolboxroughness;

I'm trying to measure surface roughness from an image. I want to carry out gaussian filtering and according to literature, gaussian filtering gives the waviness profile and subtracting the waviness from the primary profile would give the roughness profile.

Does imgaussfilt work in the same way? I obtained a filtered image and then going on to subtract gives me a black image.

function gauss
I = imread('/Users/doyindav/Desktop/qualitymap.bmp');
sigma = 1;
%cutoff = 0.08;
h = imgaussfilt(I,'Filtersize',sigma);
new_image = imsubtract(I,h);
montage([I,new_image])
end

Best Answer

Your blur wasn't big enough, plus your difference values were all zero because you didn't blur it enough. This works:

% grayImage = imread('/Users/doyindav/Desktop/qualitymap.bmp');
grayImage = imread('moon.tif'); % Demo image.
subplot(1, 3, 1);
imshow(grayImage);
[blurredImage, subtractedImage] = gauss(grayImage);
subplot(1, 3, 2);
imshow(blurredImage);
subplot(1, 3, 3);
imshow(subtractedImage, []);
function [blurredImage, new_image] = gauss(grayImage)
sigma = 15;
%cutoff = 0.08;
blurredImage = imgaussfilt(grayImage,'Filtersize', sigma);
new_image = imsubtract(grayImage, blurredImage);
end

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MATLAB: Error using .* Integers can only be combined with integers of the same class, or scalar doubles.

I think this is really what you were trying to do:

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 = 15;
% Get noise-free original gray scale image.
grayImage = imread('eight.tif');
% 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);
% Create noisy image from it.
noisyImage = imnoise(im2double(grayImage), 'Gaussian', 0, 0.01);
% Define spatial filters as a 15x15 window.
gaussianSpatialFilter = fspecial('gaussian', 15, 1.0);
% Do a filter by blurring with a Gaussian kernel in the spatial domain:
spatialDomainFilteredImage = imfilter(noisyImage, gaussianSpatialFilter, 'replicate');
% Display results:

subplot(2, 4, 1); imshow(grayImage, []); title('Original Image', 'FontSize', fontSize); axis on;
subplot(2, 4, 2); imshow(noisyImage, []); title('Noisy Image', 'FontSize', fontSize); axis on;
subplot(2, 4, 3); imshow(gaussianSpatialFilter, []); title('Blur Kernel', 'FontSize', fontSize); axis on;
subplot(2, 4, 4); imshow(spatialDomainFilteredImage, []); title('Image filtered with Gaussian in the spatial domain', 'FontSize', fontSize); axis on;
% Define frequency domain filter the same size as the image.
gaussianFreqFilter = fspecial('gaussian', [rows, columns], 0.2*rows); % Keep the low frequencies.
% gaussianFreqFilter = ones(rows, columns); % Keep all frequencies.
% Fourier transform the noisy image.
freqDomainImage = fftshift(fft2(noisyImage));
% Now do filtering in the frequency domain
filteredFreqDomain = freqDomainImage .* gaussianFreqFilter;
% Now transform back to spatial domain:
spatialImage = real(ifft2(ifftshift(filteredFreqDomain)));
% Display results:
subplot(2, 4, 5); imshow(gaussianFreqFilter, []); title('Freq Domain Filter', 'FontSize', fontSize); axis on;
subplot(2, 4, 6); imshow(log(abs(freqDomainImage)), []); title('Original Freq Domain Image', 'FontSize', fontSize); axis on;
subplot(2, 4, 7); imshow(log(abs(filteredFreqDomain)), []);  title('Filtered Freq Domain Image', 'FontSize', fontSize); axis on;
subplot(2, 4, 8); imshow(spatialImage, []); title('Back in Spatial Domain', 'FontSize', fontSize); axis on;
% Set up figure properties:
% Enlarge figure to full screen.
set(gcf, 'Units', 'Normalized', 'OuterPosition', [0, 0.04, 1, 0.96]);
% 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')

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Use subplot(), mean2(), and xlabel():

fontSize = 20;
% Make 3 images.
grayImage1 = imread('cameraman.tif');
grayImage2 = imread('moon.tif');
grayImage3 = imread('pout.tif');
% Display image 1 and mean of that image.
subplot(1, 3, 1);
imshow(grayImage1, []);
title('cameraman.tif', 'FontSize', fontSize, 'Interpreter', 'None');
mean1 = mean2(grayImage1);
caption = sprintf('Mean = %.2f', mean1);
xlabel(caption, 'FontSize', fontSize);
% 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') 
% Display image 2 and mean of that image.
subplot(1, 3, 2);
imshow(grayImage2, []);
title('moon.tif', 'FontSize', fontSize, 'Interpreter', 'None');
theMean = mean2(grayImage2);
caption = sprintf('Mean = %.2f', theMean);
xlabel(caption, 'FontSize', fontSize);
% Display image 3 and mean of that image.
subplot(1, 3, 3);
imshow(grayImage3, []);
title('pout.tif', 'FontSize', fontSize, 'Interpreter', 'None');
mean3 = mean2(grayImage3);
caption = sprintf('Mean = %.2f', mean3);
xlabel(caption, 'FontSize', fontSize);

Best Answer

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