# The algorithm of the impulse noise filtration in images based on an algorithm of community detection in graphs

## TL;DR

This paper introduces a novel impulse noise filtering algorithm for images that uses community detection in graphs, identifying and restoring only noisy pixels, and demonstrates superior performance over median filters.

## Contribution

The paper presents a new noise filtration method based on community detection in graphs, improving noise removal efficiency by 20% over median filters.

## Key findings

- The method outperforms median filter by 20% in noise removal.
- It effectively identifies and restores only noised pixels.
- Performance depends on the noise percentage in the image.

## Abstract

This article suggests an algorithm of impulse noise filtration, based on the community detection in graphs. The image is representing as non-oriented weighted graph. Each pixel of an image is corresponding to a vertex of the graph. Community detection algorithm is running on the given graph. Assumed that communities that contain only one pixel are corresponding to noised pixels of an image. Suggested method was tested with help of computer experiment. This experiment was conducted on grayscale, and on colored images, on artificial images and on photos. It is shown that the suggested method is better than median filter by 20% regardless of noise percent. Higher efficiency is justified by the fact that most of filters are changing all of image pixels, but suggested method is finding and restoring only noised pixels. The dependence of the effectiveness of the proposed method on the percentage of noise in the image is shown.