1. 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

2. Abstract Digital image filtering techniques Effectiveness of the median filter with different inputs. Scenery, objects, and people Criteria: noise reduction and extent of blurring

3. 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.

4. Larger Purpose Processing and enhancing digital images The effectiveness of the median filter on different images When to use the median filter Blurring effects

5. 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

6. Median Filter Sliding window

7. Development One module 3 by 3 sliding window Insertion Sort Ignores edges Noise introduction – percent probabibility

8. Sample Effects Noise reduction

9. Sample Effects Blurring Effects

10. Sample Effects Blurring Effects

11. Conclusions Noise elimination equally effective Reduction in quality most severe in scenery Followed by people Objects least affected