Median Filter

Introduction

Given an image, we’ll update each pixel intensity by

• Taking all pixels present in specified proximity ( by order of neighborhood ) of pixel P[x, y]
• Then computing their mean intensity, and putting it in P[x, y]

Well, we can compute median filtered image by sequentially processing each pixel of image, I. But that’s gonna take a lot of time for images with higher resolution and/ or high order of filtering value.

So, we’ll rather go for another solution - compute concurrently.

• [i] Process each pixel of image concurrently
• [ii] Process each row/ column of image matrix concurrently

Now concurrent processing can be done either by using explicit thread(s) for each job/ thread pool by scheduling submitted jobs.

But I’d like to choose [ii]. Here’s why ?

• Here we’ll be required to create/ schedule lesser number of workers
• Each worker will do a substantial amount of job
• In first case, each worker does a very small portion of total work, but we end up scheduling a lot of jobs, which eventually increases waiting time.

Current median filter implementation based on choice [ii].

Usage

• Copy below code snippet into Java source code file.

import in.itzmeanjan.filterit.ImportExportImage;
import in.itzmeanjan.filterit.filter.MedianFilter;


public class Main{

	public static void main(String [] args){
		System.out.println(
            ImportExportImage.exportImage(
                new MedianFilter().filter("sea.jpg", 1), 
                "medianFiltered.jpg"));
	}

}

• Make sure you’ve added in.itzmeanjan.filterit.jar as your project dependency.
• Now compile & run.

Results

Order-0 image is original one.

Order	Image
0
1
2
3
4
5