Adding Various Random Noise to Images Using MATLAB

Adding various random noises to images using MATLAB is a common technique that can be implemented through function calls and the proper utilization of the randn function. This implementation typically involves creating noise matrices using algorithms like Gaussian (randn), Salt-and-Pepper, or Poisson noise, then combining them with the original image matrix using element-wise operations. For Gaussian noise implementation, the code structure generally follows: noise = mean + std_dev * randn(size(image)); noisy_image = double(image) + noise; where randn generates normally distributed random numbers with zero mean and unit variance. Additionally, it's crucial to understand key programming concepts like "send" and "state" in the context of random number generation. "Send" typically refers to the process of transmitting information or data between functions or systems, while "state" refers to the current condition or configuration of a system or object - particularly important when working with random number generators where state determines the sequence of generated values. By comprehending these concepts and implementing appropriate noise algorithms, we can effectively enhance image processing applications in MATLAB, making images more realistic and interesting for various computer vision and simulation purposes. The proper management of random number generator states ensures reproducible results across different executions.