Computer Vision Demonstration Website

Electronics and Computer Science
University of Southampton

Gaussian Filtering

The Gaussian Smoothing Operator performs a weighted average of surrounding pixels based on the Gaussian distribution. It is used to remove Gaussian noise and is a realistic model of defocused lens. Sigma defines the amount of blurring. The radius slider is used to control how large the template is. Large values for sigma will only give large blurring for larger template sizes. Noise can be added using the sliders.

How it works

The operator generates a template of values that are then applied to groups of pixels in the image. These template values are defined by 2D Gaussian Equation:

Sigma defines the amount of blurring:

High sigma values require significantly more calculations per pixel

Code

Image processing classes:

• gaussianFilter.java

Noise generation classes:

• gaussianNoise.java
• condimentNoise.java

Demo framework:

• gaussianFiltering.java

The pages were designed and developed for educational purposes only, to demonstrate how computer vision techniques work. They are designed for no other purpose and neither the authors nor their institutions accept any liability concerning use of these pages.

Links

Mark Nixon & Alberto Aguado, 2002, Feature Extraction & Image Processing, Newnes

ECS | Feature Extraction & Image Processing | © 2005 University of Southampton