MATLAB – Butterworth Highpass Filter in Image Processing
Butterworth Highpass Filter (BHPF)
- is a positive constant. BHPF passes all the frequencies greater than value without attenuation and cuts off all the frequencies less than it.
- This is the transition point between H(u, v) = 1 and H(u, v) = 0, so this is termed as cutoff frequency. But instead of making a sharp cut-off (like, Ideal Highpass Filter (IHPF)), it introduces a smooth transition from 0 to 1 to reduce ringing artifacts.
- is the Euclidean Distance from any point (u, v) to the origin of the frequency plane, i.e,
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 order and cut-off frequency Step 5: Designing filter: Butterworth 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:
% MATLAB Code | Butterworth High Pass Filter % Reading input image : input_image input_image = imread( '[name of input image file].[file format]' ); % Saving the size of the input_image in pixels- % M : no of rows (height of the image) % N : no of columns (width of the image) [M, N] = size(input_image); % Getting Fourier Transform of the input_image % using MATLAB library function fft2 (2D fast fourier transform) FT_img = fft2(double(input_image)); % Assign the order value n = 2; % one can change this value accordingly % Assign Cut-off Frequency D0 = 10; % one can change this value accordingly % Designing filter u = 0:(M-1); v = 0:(N-1); idx = find(u > M/2); u(idx) = u(idx) - M; idy = find(v > N/2); v(idy) = v(idy) - N; % MATLAB library function meshgrid(v, u) returns % 2D grid which contains the coordinates of vectors % v and u. Matrix V with each row is a copy of v % and matrix U with each column is a copy of u [V, U] = meshgrid(v, u); % Calculating Euclidean Distance D = sqrt(U.^2 + V.^2); % determining the filtering mask H = 1./(1 + (D0./D).^(2*n)); % Convolution between the Fourier Transformed % image and the mask G = H.*FT_img; % Getting the resultant image by Inverse Fourier Transform % of the convoluted image using MATLAB library function % ifft2 (2D inverse fast fourier transform) output_image = real(ifft2(double(G))); % Displaying Input Image and Output Image subplot(2, 1, 1), imshow(input_image), subplot(2, 1, 2), imshow(output_image, [ ]); |
Input Image –
Output:
Note: