Removing Gaussian Noise from a Signal to Get Minimum Value

1. Why do we need Gaussian noise?
2. What is Gaussian noise in signal processing?
3. Can Gaussian noise be negative?

Why do we need Gaussian noise?

A first advantage of Gaussian noise is that the distribution itself behaves nicely. It's called the normal distribution for a reason: it has convenient properties, and is very widely used in natural and social sciences. People often use it to model random variables whose actual distribution is unknown.

What is Gaussian noise in signal processing?

Gaussian noise, named after Carl Friedrich Gauss, is a term from signal processing theory denoting a kind of signal noise that has a probability density function (pdf) equal to that of the normal distribution (which is also known as the Gaussian distribution).

Can Gaussian noise be negative?

[Gaussian] The probability distribution of the noise samples is Gaussian with a zero mean, i.e., in time domain, the samples can acquire both positive and negative values and in addition, the values close to zero have a higher chance of occurrence while the values far away from zero are less likely to appear.