Donnerstag, 18. Juli 2013

Image Sharpening using second order derivative –(Laplacian)


Prerequisite: Read EdgeDetection- fundamentals

The derivative operator Laplacian for an Image is defined as



For X-direction,

For Y-direction,


By substituting, Equations in Fig.B and Fig.C in Fig.A, we obtain the following equation







The equation  represented in terms of Mask:
0
1
0
1
-4
1
0
1
0

When the diagonals also considered then the equation becomes,



The Mask representation of the above equation,
1
1
1
1
-8
1
1
1
1

Now let’s discuss further how image sharpening is done using Laplacian.

Equation:
Where f(x,y)  is the input image
              g(x,y) is the sharpened image and
               c= -1 for the above mentioned filter masks.(fig.D and fig.E)

MATLAB CODE:

%Input Image
A=imread('coins.png');
figure,imshow(A);



%Preallocate the matrices with zeros
I1=A;
I=zeros(size(A));
I2=zeros(size(A));

%Filter Masks
F1=[0 1 0;1 -4 1; 0 1 0];
F2=[1 1 1;1 -8 1; 1 1 1];

%Padarray with zeros
A=padarray(A,[1,1]);
A=double(A);

%Implementation of the equation in Fig.D
for i=1:size(A,1)-2
    for j=1:size(A,2)-2
       
        I(i,j)=sum(sum(F1.*A(i:i+2,j:j+2)));
       
    end
end

I=uint8(I);
figure,imshow(I);title('Filtered Image');









The Laplacian derivative equation has produced grayish edge lines and other areas are made dark(background)

%Sharpenend Image
%Refer Equation in Fig.F
B=I1-I;
figure,imshow(B);title('Sharpened Image');




The Filter Image is combined with the Original input image thus the background is preserved and the sharpened image is obtained .



For a Filter Mask that includes Diagonal,


1
1
1
1
-8
1
1
1
1

Filtered Image:


Sharpened Image:



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