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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