# Gaussian Noise

If you have ever worked on a Computer Vision project, you might know that using augmentations to diversify the dataset is the best practice. On this page, we will:

- Сover the Gaussian Noise augmentation;
- Check out its parameters;
- See how Gaussian Noise affects an image;
- And check out how to work with Gaussian Noise using Python through the Albumentations library.

Let’s jump in!

##
**Gaussian Noise explained**

To understand what Gaussian Noise is, let’s first observe the concept of noise in digital images.

Noise usually stands for a random variation in the** brightness or color** of the image. In the case of digital images, noise can be produced due to different reasons:

- The image sensor is broken or affected by external factors;
- Lack of light or overheating of the device at the moment of taking a photo;
- Interference of the transmission channel, and so on.

The noise might be added or multiplied to the image. Here is the formula for the **Additive Noise Model**, where:

- x and y are the coordinates of the pixel to which the noise is applied;
- s(x, y) is the intensity of the original image;
- n(x, y) is the noise added to the original image;
- w(x,y) is the distorted image received after the noise is applied.

Likewise, the Multiplicative Noise Model multiplies the original signal by the noise signal.

Gaussian Noise is a statistical noise with a Gaussian (normal) distribution. It means that the noise values are distributed in a normal Gaussian way.

The Gaussian noise is added to the original image. The probability density function p of a Gaussian random variable z is calculated by the following formula:

- where z represents the grey level;
- u - the mean value;
- and sigma - the standard deviation.

The Gaussian Noise data augmentation tool adds Gaussian noise to the training images to make the model robust against such noises.

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**Parameters**

**Variance limit -**sets the variance**Mean**- sets the mean of the noise. The higher the mean value, the brighter the image will be. The specified value must fall between [0.0, 255.0];**Probability of applying transform**- sets the probability of the augmentation being applied to an image. If you want to apply Gaussian Noise to all images, select a probability of 1.