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Image processing demo - high pass filter
version ( KB) by Bogumila & Zbigniew MROZEK
Graphic processing with MATLAB only (no toolboxes). High-pass filtering process example included.
Low-pass filtering is easy and always works fine (not included here). High-pass filtering needs conversion of numbers from integer to float before processing and then conversion back to integer -- to keep numbers within their limits. Lena.jpg example is included. Presented on EAEEIE conference ("Re-inventing Image Processing teaching") and published in a book MATLAB and Simulink by Bogumila & Zbigniew MROZEK, https://helion.pl/ksiazki/matsi4
Cite As
Bogumila & Zbigniew MROZEK (). Image processing demo - high pass filter (https://www.mathworks.com/matlabcentral/fileexchange/image-processing-demo-high-pass-filter), MATLAB Central File Exchange. Retrieved .
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How do I use IMFILTER to high pass filter an image?
clc;
close all;
imtool close all;
clear;
workspace;
fontSize = 20;
folder = fullfile(matlabroot, '\toolbox\images\imdemos');
baseFileName = 'cameraman.tif';
fullFileName = fullfile(folder, baseFileName);
fullFileName = fullfile(folder, baseFileName);
if ~exist(fullFileName, 'file')
fullFileName = baseFileName;
if ~exist(fullFileName, 'file')
errorMessage = sprintf('Error: %s does not exist.', fullFileName);
uiwait(warndlg(errorMessage));
return;
end
end
grayImage = imread(fullFileName);
[rows columns numberOfColorBands] = size(grayImage);
subplot(2, 2, 1);
imshow(grayImage, []);
title('Original Grayscale Image', 'FontSize', fontSize);
set(gcf, 'Position', get(0,'Screensize'));
set(gcf,'name','Demo by ImageAnalyst','numbertitle','off')
kernel1 = -1 * ones(3)/9;
kernel1(2,2) = 8/9
filteredImage = imfilter(single(grayImage), kernel1);
subplot(2, 2, 2);
imshow(filteredImage, []);
title('Filtered Image', 'FontSize', fontSize);
kernel2 = [-1 -2 -1; -2 12 -2; -1 -2 -1]/16;
filteredImage = imfilter(single(grayImage), kernel2);
subplot(2, 2, 3);
imshow(filteredImage, []);
title('Filtered Image', 'FontSize', fontSize);
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MATLAB Ideal Highpass Filter in Image Processing
In the field of Image Processing, Ideal Highpass Filter (IHPF) is used for image sharpening in the frequency domain. Image Sharpening is a technique to enhance the fine details and highlight the edges in a digital image. It removes low-frequency components from an image and preserves high-frequency components.
This ideal highpass filter is the reverse operation of the ideal lowpass filter. It can be determined using the following relation-
where, is the transfer function of the highpass filter and
is the transfer function of the corresponding lowpass filter.
The transfer function of the IHPF can be specified by the function-
Where,
Approach:
Step 1: Input Read an image
Step 2: Saving the size of the input image in pixels
Step 3: Get the Fourier Transform of the input_image
Step 4: Assign the Cut-off Frequency
Step 5: Designing filter: Ideal High Pass Filter
Step 6: Convolution between the Fourier Transformed input image and the filtering mask
Step 7: Take Inverse Fourier Transform of the convoluted image
Step 8: Display the resultant image as output
Implementation in MATLAB:
Input Image
Output:
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Pass high filter image matlab
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