An Implementation of the Median Filter

and Its Effectiveness on Different Kinds of Images

Kevin Liu

2006-2007

Thomas Jefferson High School for Science and Technology

Abstract

Digital image filtering techniques

Effectiveness of the median filter with different inputs.

Scenery, objects, and people

Criteria: noise reduction and extent of blurring

Introduction andBackground

Digital image processing first developed in the 1960's

Clear out noise or useless and distracting information in pictures

Missing pixels and wrong pixels

Inevitable when converting analog information into a digital form

transmission of image files from one location to another through physical mediums or through wireless communication.

Larger Purpose

Processing and enhancing digital images

The effectiveness of the median filter on different images

When to use the median filter

Blurring effects

Procedures and Methods

Varying intensity – size of window

Java – low number of Java classes

Noise introduction Module

Objects, people, scenery

Noise elimination quality

Extent of reduction in quality

Median Filter

Sliding window

Development

One module

3 by 3 sliding window

Insertion Sort

Ignores edges

Noise introduction – percent probabibility

Sample Effects

Noise reduction Noise reduction

Sample Effects

Blurring Effects

Sample Effects

Blurring Effects

Conclusions

Noise elimination equally effective

Reduction in quality most severe in scenery

Followed by people

Objects least affected