Adding Various Types of Noise to Images

In this text, we demonstrate how to add various types of noise to images using MATLAB. By incorporating salt-and-pepper noise, Gaussian noise, multiplicative noise, and other noise types, we can simulate real-world image degradation under different environmental conditions. This approach helps researchers better understand and evaluate the robustness and performance of image processing algorithms. In practical applications, handling images contaminated with diverse noise patterns is essential, making knowledge of both noise addition and removal techniques critically important.

Implementation details include: - Salt-and-pepper noise: Randomly replacing pixel values with maximum (salt) or minimum (pepper) intensity values using probability thresholds - Gaussian noise: Adding normally distributed random values with specified mean and variance to pixel intensities using MATLAB's 'imnoise' function - Multiplicative noise: Applying speckle noise through pixel-wise multiplication with random patterns, commonly modeled as I*(1 + noise) where I is the original image - Additional noise types like Poisson noise for photon-limited imaging scenarios can be implemented using appropriate statistical distributions

Key MATLAB functions involved: imnoise() for built-in noise addition capabilities, rand() and randn() for custom noise generation, image processing toolbox functions for advanced implementations