Using the code below create code for the buttons to produce the four image smoothing filters 55 mean filter, 55 Gaussian filter with user-specified  parameter, 55 median filter, and 55 Kuwahara filter. Making use of the most efficient implementation based on the property of the filters is required. The end result must be the image below with all the buttons working.

 import java.awt.*; import java.awt.event.*; import java.awt.image.*; import java.io.*; import javax.imageio.*; // Canvas for image display class ImageCanvas extends Canvas { BufferedImage image; // initialize the image and mouse control public ImageCanvas(BufferedImage input) { image = input; addMouseListener(new ClickListener()); } public ImageCanvas(int width, int height) { image = new BufferedImage(width,height,BufferedImage.TYPE_INT_RGB); addMouseListener(new ClickListener()); } // redraw the canvas public void paint(Graphics g) { // draw boundary g.setColor(Color.gray); g.drawRect(0, 0, getWidth()-1, getHeight()-1); // compute the offset of the image. int xoffset = (getWidth() - image.getWidth()) / 2; int yoffset = (getHeight() - image.getHeight()) / 2; g.drawImage(image, xoffset, yoffset, this); } // change the image and redraw the canvas public void resetImage(Image input) { image = new BufferedImage(input.getWidth(null), input.getHeight(null), BufferedImage.TYPE_INT_RGB); Graphics2D g2D = image.createGraphics(); g2D.drawImage(input, 0, 0, null); repaint(); } // change the image and redraw the canvas public void resetBuffer(int width, int height) { image = new BufferedImage(width,height,BufferedImage.TYPE_INT_RGB); Graphics2D g2D = image.createGraphics(); } // listen to mouse click class ClickListener extends MouseAdapter { public void mouseClicked(MouseEvent e) { if ( e.getClickCount() == 2 && e.getButton() == MouseEvent.BUTTON3 ) try { ImageIO.write(image, "png", new File("saved.png")); } catch ( Exception ex ) { ex.printStackTrace(); } } } } 

import java.util.*; import java.awt.*; import java.awt.event.*; import java.awt.image.*; import java.io.*; import javax.imageio.*; // Main class public class SmoothingFilter extends Frame implements ActionListener { BufferedImage input; ImageCanvas source, target; TextField texSigma; int width, height; // Constructor public SmoothingFilter(String name) { super("Smoothing Filters"); // load image try { input = ImageIO.read(new File(name)); } catch ( Exception ex ) { ex.printStackTrace(); } width = input.getWidth(); height = input.getHeight(); // prepare the panel for image canvas. Panel main = new Panel(); source = new ImageCanvas(input); target = new ImageCanvas(input); main.setLayout(new GridLayout(1, 2, 10, 10)); main.add(source); main.add(target); // prepare the panel for buttons. Panel controls = new Panel(); Button button = new Button("Add noise"); button.addActionListener(this); controls.add(button); button = new Button("5x5 mean"); button.addActionListener(this); controls.add(button); controls.add(new Label("Sigma:")); texSigma = new TextField("1", 1); controls.add(texSigma); button = new Button("5x5 Gaussian"); button.addActionListener(this); controls.add(button); button = new Button("5x5 median"); button.addActionListener(this); controls.add(button); button = new Button("5x5 Kuwahara"); button.addActionListener(this); controls.add(button); // add two panels add("Center", main); add("South", controls); addWindowListener(new ExitListener()); setSize(width*2+100, height+100); setVisible(true); } class ExitListener extends WindowAdapter { public void windowClosing(WindowEvent e) { System.exit(0); } } // Action listener for button click events public void actionPerformed(ActionEvent e) { // example -- add random noise if ( ((Button)e.getSource()).getLabel().equals("Add noise") ) { Random rand = new Random(); int dev = 64; for ( int y=0, i=0 ; y 255 ? 255 : red; green = green  255 ? 255 : green; blue = blue  255 ? 255 : blue; source.image.setRGB(x, y, (new Color(red, green, blue)).getRGB()); } source.repaint(); } } public static void main(String[] args) { new SmoothingFilter(args.length==1 ? args[0] : "baboon.png"); } } 

IAddnoise 55 mean Sigma: -.5x5 Gaussian 5x5 median 5x5 Kuwahara IAddnoise 55 mean Sigma: -.5x5 Gaussian 5x5 median 5x5 Kuwahara