Skip to content

Blurring and Unsharp Masking in Java

This java code loads an image and blurs it using a very simple boxcar (rectangular) blurring method. Then it sharpens the image using unsharp mask technique. for more information on unsharp masking see Wikipedia article.

Parameters you can play with:

(left, top), (right, bottom) defines a selection of original image
usmAmount and usmThreshold defines the unsharp mask parameters
(boxWidth, boxHeight) defines the small box for boxcar filter kernel

Please note: This code is written in the C-like procedural manner using java static methods. Apart from loading and saving images using java Image API functions all the processing is done within the code. Therefore the code can be converted any programming language very easily.

Freely distributable
(c) 2011 Pasan Hettiarachchi
(c) Microsolutions
Any questions or comments?
This program reads a raster image to a BufferedImage object. Then the RGB values for each
pixel is loaded in to a 2D array. A slection of this 2D image (left, top, width, height) is
processed with a bocar filter of given size. A simple usm technique is used then to sharpen
the original and the result is also saved.

No Java's built-in 2D API functions are used except for reading and writing raster graphics

import java.awt.image.BufferedImage;
import javax.imageio.ImageIO;

public class ImageFilter {

    public static void main(String[] args) {

        String imageFilename = "myimage.jpg";

        File originalImgFile = new File(imageFilename);
        File blurredImgFile = new File("blurred_" + imageFilename);
        File usmImgFile = new File("sharpened_" + imageFilename);

        BufferedImage loadedImage = null;
        long startTime = System.currentTimeMillis();
        System.out.printf("At %tT.%<tL Loading image '%s' from disk...\n", startTime, imageFilename);
        try {
            loadedImage =;
        } catch (Exception e) {
            System.out.println("Eror opening image" + e.toString());
        int imgWidth = loadedImage.getWidth();
        int imgHeight = loadedImage.getHeight();

        System.out.printf("Loaded image '%s' of size (%d x %d) pixels to an BuffredImage object..\n",
                imageFilename, imgWidth, imgHeight);

        //the following line defines the small box for filter kernel
        int boxWidth = 20, boxHeight = 20;
        //the parameters, (left, top, seWidth, selHeight) defines a selection of original image
        int left = 50, top = 50; //left and top should be more than half of box width and height
        //selection width and height is chosen to center the selection
        //can also be used any given selection
        int right = (imgWidth - left), bottom = (imgHeight - top);

        //USM filter parameters
        float usmAmount = 0.6f;
        int usmThreshold = 3;

        int[][] origPixels = loadFromImgBuffer(loadedImage);
        System.out.printf("Loaded BuffredImage RGB data to a 2D array...\n");
        int[][] blurredPixels = new int[imgWidth][imgHeight];

        System.out.printf("Now applying boxcar blur filter. Please wait...\n");
        //applying the boxcar filter with given parameters and save the result in
        //another 2D array called blurredImage
        try {
            //both the loadedImage and blurredPixels will hold the result of the boxcar function and
            boxCar(loadedImage, origPixels, blurredPixels, left, top,
                    right, bottom, boxWidth, boxHeight);
            System.out.printf("Filtering finished, saving the blurred image.\n");
            //save the loadedImage imagebuffer to a new file
            try {
                ImageIO.write(loadedImage, "jpg", blurredImgFile);
                System.out.printf("Saved the blurred image '%s'\n", blurredImgFile.getName());
            } catch (Exception e) {
                System.out.println("Eror saving blurred image" + e.toString());

            System.out.printf("Now applying unsharp mask filter. Please wait...\n");
            //sharpenes the image using USM and it back to loadedImage ImageBuffer object
            unsharpMask(loadedImage, origPixels, blurredPixels, left, top,
                    right, bottom, usmAmount, usmThreshold);

            //save the imagebuffer to a new file
            try {
                ImageIO.write(loadedImage, "jpg", usmImgFile);
                long elapsed = System.currentTimeMillis() - startTime;
                System.out.printf("Elapsed %5d mS, saved the sharpened image '%s'\n", elapsed, usmImgFile.getName());
            } catch (Exception e) {
                System.out.println("Eror saving sharpened image" + e.toString());

        } catch (Exception e) {
            System.out.printf("Image selection is probably too large:(%d, %d, %d, %d)\n%s\n",
                    left, top, right, bottom, e.toString());

    //This is the boxcar filter function accepting a 2D array of RGB pixels,
    //A selection for the filter area and a filter kernel size.
    //it also fills an imagebuffer object with blurred image data, which can be saved to disk
    private static void boxCar(BufferedImage loadedImage, int[][] origPixels,
            int[][] blurredPixels, int left, int top, int right, int bottom,
            int filtX, int filtY) throws ArrayIndexOutOfBoundsException {

        //a boundaries of a small 2d array containing adjacent box of pixels for a given
        //pixel is sent to blurPixels function. This function processes for R,G,B and
        //alpha bytes in each pixel in the box seperately and reconstucts the averaged pixel

        for (int j = top; j < bottom; j++) {
            for (int i = left; i < right; i++) {

                //blur pixels using averaging a box of pixels surrounding the given
                //pixel (i,j) and saving the result in both blurredPixels and
                //loadedImage ImageBuffer object
                blurredPixels[i][j] = blurPixels(origPixels, (i - filtX / 2), (j - filtY / 2),
                        (i + filtX / 2), (j + filtY / 2));
                loadedImage.setRGB(i, j, blurredPixels[i][j]);

    //This function returns a RGB value taking the mean of
    //RGB values of each pixel in the filter kernel box
    private static int blurPixels(int[][] origPixels, int left, int top, int right, int bottom) {
        //transperency is not considered
        int alpha = 0xff000000, red = 0, green = 0, blue = 0;
        int boxSize = (right - left) * (bottom - top);

        //the following nested for loops takes the sum of RGB components of each
        //pixels in the box.
        for (int q = top; q < bottom; q++) {
            for (int p = left; p < right; p++) {
                int pixel = origPixels[p][q];
                red += ((pixel >> 16) & 0xff);
                green += ((pixel >> 8 ) & 0xff);
                blue += ((pixel) & 0xff);
        //average is computed using integer arithmetic. If the box size is too large
        //this routine will fail. max box size = (INT_Max/256) = 8,388,608
        red /= boxSize;
        green /= boxSize;
        blue /= boxSize;
        //returns the reconstructed pixel back
        return (alpha | (red << 16) | (green << 8 ) | blue);

    //this function calculates the unsharpmask by substracting originalpixels
    //by blurred pixels. Then it sharpenes the original image by adding a
    //weighted amount of the unsharpmask back to the original depending on
    //the given threshold value
    private static void unsharpMask(BufferedImage usmImage,
            int[][] origPixels, int[][] blurredPixels, int left,
            int top, int right, int bottom, float amount, int threshold) {

        int orgRed = 0, orgGreen = 0, orgBlue = 0;
        int blurredRed = 0, blurredGreen = 0, blurredBlue = 0;
        int usmPixel = 0;
        int alpha = 0xFF000000; //transperency is not considered and always zero

        for (int j = top; j < bottom; j++) {
            for (int i = left; i < right; i++) {
                int origPixel = origPixels[i][j], blurredPixel = blurredPixels[i][j];

                //seperate RGB values of original and blurred pixels into seperate R,G and B values
                orgRed = ((origPixel >> 16) & 0xff);
                orgGreen = ((origPixel >> 8 ) & 0xff);
                orgBlue = (origPixel & 0xff);
                blurredRed = ((blurredPixel >> 16) & 0xff);
                blurredGreen = ((blurredPixel >> 8 ) & 0xff);
                blurredBlue = (blurredPixel & 0xff);

                //If the absolute val. of difference between original and blurred
                //values are greater than the given threshold add weighed difference
                //back to the original pixel. If the result is outside (0-255),
                //change it back to the corresponding margin 0 or 255
                if (Math.abs(orgRed - blurredRed) >= threshold) {
                    orgRed = (int) (amount * (orgRed - blurredRed) + orgRed);
                    orgRed = orgRed > 255 ? 255 : orgRed < 0 ? 0 : orgRed;

                if (Math.abs(orgGreen - blurredGreen) >= threshold) {
                    orgGreen = (int) (amount * (orgGreen - blurredGreen) + orgGreen);
                    orgGreen = orgGreen > 255 ? 255 : orgGreen < 0 ? 0 : orgGreen;

                if (Math.abs(orgBlue - blurredBlue) >= threshold) {
                    orgBlue = (int) (amount * (orgBlue - blurredBlue) + orgBlue);
                    orgBlue = orgBlue > 255 ? 255 : orgBlue < 0 ? 0 : orgBlue;

                usmPixel = (alpha | (orgRed << 16) | (orgGreen << 8 ) | orgBlue);
                usmImage.setRGB(i, j, usmPixel);

    //function to load ARGB values of each pixel in to a 2D array
    static int[][] loadFromImgBuffer(BufferedImage image) {
        int width = image.getWidth(), height = image.getHeight();
        int[][] pixels = new int[width][height];
        for (int j = 0; j < height; j++) {
            for (int i = 0; i < width; i++) {
                pixels[i][j] = image.getRGB(i, j);
        return pixels;

Download java sourcecode

Leave a Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.