An Implementation of the Median Filter and Its Effectiveness on Different Kinds of Images Kevin Liu 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 and Background 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

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