MATLAB Noise Generator for Image Processing

This MATLAB program implements a versatile noise generator that can generate Gaussian, salt-and-pepper, additive, and multiplicative noise, and apply these noise types to images. The program offers multiple noise generation options to accommodate various requirements. Users can generate Gaussian noise to simulate real-world random noise patterns, typically implemented using MATLAB's randn() function with configurable mean and variance parameters. Alternatively, users can generate salt-and-pepper noise to simulate signal loss during image transmission, which is commonly achieved by randomly setting pixel values to minimum (pepper) or maximum (salt) intensities based on a specified noise density. Additionally, the program allows generation of additive noise (where noise values are added directly to pixel intensities) and multiplicative noise (where noise values multiply the original pixel values), both essential for testing the robustness of image processing algorithms. By applying these noise types to images, users can better understand how image processing algorithms perform under different noise conditions. The program also includes adjustable noise intensity options, implemented through parameter controls that allow users to modify noise levels according to their specific needs. For instance, Gaussian noise intensity can be controlled via variance parameters, while salt-and-pepper noise density can be adjusted through probability thresholds. Overall, this program provides a convenient and flexible noise generation tool suitable for image processing applications and algorithm testing scenarios. The implementation likely utilizes MATLAB's matrix operations and random number generation functions to efficiently create and apply various noise patterns while maintaining image dimensions and data types.