Multiple Algorithms for Adding Noise to Images

In image processing, multiple algorithms can be employed to add noise to images. Two common noise types are salt-and-pepper noise and Gaussian noise. When using MATLAB for image processing, the built-in Gaussian noise function may yield unexpected results due to normalization issues. Therefore, it's typically preferable to implement custom code for noise addition. Implementation approach: For salt-and-pepper noise, randomly select pixels and set them to either minimum (pepper) or maximum (salt) intensity values. For Gaussian noise, generate random values from a normal distribution and add them to pixel values while considering proper intensity scaling to avoid clipping. Key functions: Custom implementations typically use rand() or randn() functions for random number generation, and imnoise() can be used as reference but with caution for Gaussian noise applications. Proper intensity range management is crucial when adding noise to prevent values from exceeding valid pixel ranges.