Matlab image high pass filter

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);

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: