Digital Image Processing Using Scilab Pdf 【Exclusive Deal】

// 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

// Install SIVP from ATOMS (Scilab’s package manager) atomsInstall("SIVP") // Restart Scilab after installation // Load the toolbox exec("SCI/modules/sivp/macros/sivp_loader.sce", -1) Alternatively, use core functions ( imread , imshow , imwrite ) available in recent Scilab versions. 3.1 Read, Display, and Write Images // Read an image img = imread('camera.jpg'); // Display image imshow(img);

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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 // Apply filter F_filtered = F_shifted

// Write image to disk imwrite(img, 'output.png');

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); Edge Detection 6

// 3. Denoise with median filter img = medfilt2(img, [3 3]);

// Gradient magnitude edge_magnitude = sqrt(Gx.^2 + Gy.^2); imshow(uint8(edge_magnitude)); // Prewitt prewitt_x = [-1 0 1; -1 0 1; -1 0 1]; // Laplacian (second derivative) laplacian = [0 -1 0; -1 4 -1; 0 -1 0]; edges_laplacian = imfilter(gray_img, laplacian); 7. Morphological Operations Requires binary images.

// Opening (erosion followed by dilation) opened = imopen(binary, se);

Article ID: DIP-SCILAB-01 Target Audience: Engineering students, researchers, hobbyists Software Required: Scilab 6.1+ with SIVP (Scilab Image and Video Processing) toolbox 1. Introduction Digital Image Processing (DIP) involves manipulating digital images using computer algorithms. While MATLAB is the industry standard, Scilab —a free, open-source alternative—provides powerful capabilities for DIP through its SIVP (Scilab Image and Video Processing) toolbox and core functions.