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:
Let’s jump in!
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 noise might be added or multiplied to the image. Here is the formula for the Additive Noise Model, where:
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:
The Gaussian Noise data augmentation tool adds Gaussian noise to the training images to make the model robust against such noises.
import albumentations as albu from PIL import Image import numpy as np transform =albu.GaussianNoise(var_limit=(10,50),mean=0,p=0.5) image = np.array(Image.open('/some/image/file/path')) image = transform(image=image)['image'] # Now the image is transformed and ready to be accepted by the model