Code Implementation for Generating Various Types of Noise - Poisson

In signal processing and image analysis, simulating different types of noise is a critical step. MATLAB provides multiple built-in functions to generate common types of noise. Below are implementation approaches for three typical noise types with enhanced code-related descriptions: Poisson Noise Poisson noise commonly occurs in photon counting scenarios (such as low-light images), characterized by its variance being equal to the mean. In MATLAB, it can be generated using the `poissrnd` function. The implementation requires normalizing the original signal to the desired intensity range first, then applying the Poisson random process. The key function `poissrnd(lambda)` generates Poisson-distributed random numbers where lambda represents the mean parameter, which should be scaled according to the signal intensity. Gaussian Noise Gaussian noise (also known as normal noise) is the most prevalent additive noise, characterized by its mean (μ) and standard deviation (σ). Implementation involves using the `randn` function to generate Gaussian distribution with zero mean and unit variance, followed by linear transformation to adjust σ and μ. The standard approach is: noise = μ + σ*randn(size(signal)). This type of noise is commonly added to signals or images to simulate sensor noise in practical applications. Impulse Noise Impulse noise manifests as randomly occurring extreme values (with salt-and-pepper noise being a specific case). Implementation involves replacing pixel values at random positions. The core steps include: determining noise density (e.g., 5% of pixels), randomly selecting positions, and setting these points to minimum values (pepper noise) or maximum values (salt noise). The algorithm typically uses random number generation and logical indexing to efficiently modify specific pixel values. These three noise generation methodologies can be used separately or combined, making them suitable for scenarios such as algorithm robustness testing and denoising method evaluation. In practical applications, careful attention must be paid to how noise parameters (such as intensity and density) affect data fidelity. Proper parameter selection ensures realistic simulation while maintaining the essential characteristics of the original data.