Filtre Gaussien (Filtre Gaussien) - Implementation using Python

Gaussian filter

Noise is a parasite that randomly presents in the image for physical (brightness, noise in hardware, sensor temperature, etc.) or digital (during image processing) reasons.

The Gaussian filter is a filter well known in the field of image processing which makes it possible to eliminate noise from a noisy image. It is based on a mathematical function g(x), which is frequently employed in statistical distributions.

In the case of a two-dimensional picture, x, and y:

With σ: standard derivation: presents the Gaussian bell width.

• Sigma <1 is used to reduce noise in an image
• Sigma> 1 is used to make an image for use in the unsharp mask.

The larger the sigma, the more remarkable the unsharp mask.

Implementation Using Python

In this part, we show how to apply a Gaussian filter to a noisy image using a basic demonstration, altering the radius value each time.

Steps

• The first step is to import the libraries to use;

from PIL import Image
from PIL import ImageFilter

• The next step is to import the images;

img1=Image.open("lena_original.png") img2=Image.open("lena_bruit_gaussien.png")

img1.show()

img2.show()

• The last step is to apply the gaussian filter on the images using different threshold.

#--------------------------------- filtre gaussien------------------ img2.filter(ImageFilter.GaussianBlur(radius=2)).show() img2.filter(ImageFilter.GaussianBlur(radius=3)).show() img2.filter(ImageFilter.GaussianBlur(radius=4)).show()