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// Low-pass filter in frequency domain [m, n] = size(gray_img); cx = m/2; cy = n/2; radius = 30; H = zeros(m, n); for i = 1:m for j = 1:n if sqrt((i-cx)^2 + (j-cy)^2) <= radius H(i, j) = 1; end end end
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// Threshold to create binary image binary = gray_img > 128; // Structuring element (disk of radius 3) se = [0 1 0; 1 1 1; 0 1 0]; digital image processing using scilab pdf
Creative Commons Attribution 4.0 International (CC BY 4.0) Last updated: 2025
// Compute histogram hist = imhist(gray_img); plot(hist); // Apply histogram equalization eq_img = histeq(gray_img); imshow(eq_img); min_val = min(gray_img); max_val = max(gray_img); stretched = (gray_img - min_val) / (max_val - min_val) * 255; 4.3 Gamma Correction gamma = 0.5; // darkens midtones corrected = 255 * (double(gray_img)/255)^gamma; 5. Filtering and Noise Reduction 5.1 Adding Noise noisy_img = imnoise(gray_img, 'gaussian', 0, 0.01); noisy_img = imnoise(gray_img, 'salt & pepper', 0.05); 5.2 Mean Filter (Low-pass) // 3x3 averaging kernel h = (1/9) * ones(3,3); filtered = imfilter(gray_img, h); 5.3 Median Filter (Non-linear) Better for salt-and-pepper noise: // Low-pass filter in frequency domain [m, n]
// Apply filter F_filtered = F_shifted .* H; F_restored = ifftshift(F_filtered); filtered_img = abs(ifft2(F_restored)); imshow(uint8(filtered_img)); // Full image processing pipeline function processed = process_image(path) // 1. Read img = imread(path); // 2. Convert to grayscale if size(img, 3) == 3 img = rgb2gray(img); end
median_filtered = medfilt2(gray_img, [3 3]); // Create Gaussian kernel (approx) gaussian_kernel = [1 2 1; 2 4 2; 1 2 1] / 16; gaussian_filtered = imfilter(gray_img, gaussian_kernel); 6. Edge Detection 6.1 Sobel Operator // Sobel kernels sobel_x = [-1 0 1; -2 0 2; -1 0 1]; sobel_y = [-1 -2 -1; 0 0 0; 1 2 1]; Gx = imfilter(double(gray_img), sobel_x); Gy = imfilter(double(gray_img), sobel_y); Filtering and Noise Reduction 5
// Get image dimensions (rows, cols, channels) size(img) gray_img = rgb2gray(img); imshow(gray_img); 3.3 Access and Modify Pixels // Access pixel at row 100, column 150 pixel = img(100, 150, :); // Set a region of interest to black img(50:100, 50:100, :) = 0; 4. Image Enhancement 4.1 Histogram Equalization Improves contrast by spreading intensity values.
// 3. Denoise with median filter img = medfilt2(img, [3 3]);
// 4. Enhance contrast img = histeq(img);