Java DIP - Weighted Average Filter

In weighted average filter, we gave more weight to the center value, due to which the contribution of center becomes more than the rest of the values. Due to weighted average filtering, we can control the blurring of image.
We use OpenCV function filter2D to apply weighted average filter to images. It can be found under Imgproc package. Its syntax is given below −
filter2D(src, dst, depth , kernel, anchor, delta, BORDER_DEFAULT );

The function arguments are described below −
src
It is source image.
dst
It is destination image.
ddepth
It is the depth of dst. A negative value (such as -1) indicates that the depth is the same as the source.
kernel
It is the kernel to be scanned through the image.
anchor
It is the position of the anchor relative to its kernel. The location Point(-1, -1) indicates the center by default.
delta
It is a value to be added to each pixel during the convolution. By default it is 0.
BORDER_DEFAULT
We let this value by default.
Apart from the filter2D() method, there are other methods provide by the Imgproc class. They are described briefly −
cvtColor(Mat src, Mat dst, int code, int dstCn)
It converts an image from one color space to another.
dilate(Mat src, Mat dst, Mat kernel)
It dilates an image by using a specific structuring element.
equalizeHist(Mat src, Mat dst)
It equalizes the histogram of a grayscale image.
filter2D(Mat src, Mat dst, int depth, Mat kernel, Point anchor, double delta)
It convolves an image with the kernel.
GaussianBlur(Mat src, Mat dst, Size ksize, double sigmaX)
It blurs an image using a Gaussian filter.
integral(Mat src, Mat sum)
It calculates the integral of an image.
The following example demonstrates the use of Imgproc class to apply weighted average filter to an image of Graycale.
import org.opencv.core.Core;
import org.opencv.core.CvType;
import org.opencv.core.Mat;

import org.opencv.highgui.Highgui;
import org.opencv.imgproc.Imgproc;

public class convolution {
   public static void main( String[] args ) {
   
      try {
         int kernelSize = 9;
         System.loadLibrary( Core.NATIVE_LIBRARY_NAME );
         
         Mat source = Highgui.imread("grayscale.jpg",  Highgui.CV_LOAD_IMAGE_GRAYSCALE);
         Mat destination = new Mat(source.rows(),source.cols(),source.type());
         
         Mat kernel = Mat.ones(kernelSize,kernelSize, CvType.CV_32F) {	      
         
         for(int i=0; i<kernel.rows(); i++) {
            for(int j=0; j<kernel.cols(); j++) {

               double[] m = kernel.get(i, j);

               for(int k =0; k<m.length; k++) {

                  if(i==1 && j==1) {
                     m[k] = 10/18;
                  }
                  else{
                     m[k] = m[k]/(18);
                  }
               }
               kernel.put(i,j, m);

               }
            }	
         };	      
         
         Imgproc.filter2D(source, destination, -1, kernel);
         Highgui.imwrite("output.jpg", destination);
         
      } catch (Exception e) {
         System.out.println("Error: " + e.getMessage());
      }
   }
}
When you execute the given code, the following output is seen −
This original image is convolved with the weighted average filter as given below −

    16 Lectures 
     2 hours 

 Malhar Lathkar

    19 Lectures 
     5 hours 

 Malhar Lathkar

    25 Lectures 
     2.5 hours 

 Anadi Sharma

    126 Lectures 
     7 hours 

  Tushar Kale

    119 Lectures 
     17.5 hours 

 Monica Mittal

    76 Lectures 
     7 hours 

 Arnab Chakraborty
 Print
 Add Notes
 Bookmark this page