Image processing(Filters)

1. Filtering an image is to apply a
convolution operation to an
image to achieve: blurring,
sharpening , edge extraction or
noise removal.
FILTERING OF IMAGES

2. INPUT MATRIX TO BE TAKEN:
 Sumw=(w1+w2+w3+w4+w5+w6+w7+w8+w9)

3. RESULTANT MATRIX:

4.  The simplest convolution kernel or filter is
of size 3x3 with equal weights and is
represented as follows:

5. LOW PASS FILTERS
 This filter produces a simple average (or arithmetic
mean)of the nearest neighbors of each pixel in the
image.
 It is one of a class of what are known as low pass filters.
 They pass low frequencies in the image or equivalently
pass long wavelengths in the image

6. LOW PASS FILTERS
 It correspond to abrupt changes in image intensity, i.e.
edges. Thus we get blurring.
 Also, because it is replacing each pixel with an average
of the pixels in its local neighborhood, one can
understand why it tends to blur the image.
 Blurring is typical of low pass filters.

7. CODE:

8. INPUT GRAYSCALE IMAGE:

9. MATLAB OUTPUT

10. EFFECT OF USING LARGER MATRIX:
 The result of using a larger filter(here 10X10) will be that
the low pass filtered image is blurrier than for the 5x5
case and the high pass filtered image will have thicker
edges.

11. HIGH PASS FILTERS:
 It passes only short wavelengths, i.e. where there
are abrupt changes in intensity
 It removes long wavelengths, i.e. where the
image intensity is varying slowly.
 Thus when applied to an image, it shows only
the edges in the image.

12. HIGH PASS FILTERS:
 High pass filtering of an image can be achieved
by the application of a low pass filter to the
image and subsequently subtraction of the low
pass filtered result from the image
 In abbreviated terms, this is H = I – L, where H =
highpass filtered image, I = original image and L
= low pass filtered image.

13. CODE:
 j=zeros(5)
 j(3,3)=1
 h=ones(5)/25;
 j=j-h;
 g=filter2(j,gs,'same');
 imshow(g/255)

14. OUTPUT:

15. 3. FFT Filters:
 FFT Filters provide precisely controlled low- and high-pass
filtering (smoothing and sharpening, respectively) using a
Butterworth characteristic.
 The image is converted into spatial frequencies using a
Fast Fourier Transform, the appropriate filter is applied.
 Then the image is converted back using an inverse FFT.

16. PREDEFINED FILTERS IN MATLAB:
 Predefined function used is fspecial:
 Types available:
 Averaging

17. AVERAGING:
 Code:
 f = fspecial ('average')
 I = imread ('tulips.jpg');
 gs = rgb2gray(i);
 o = filter2(f,gs);
 imshow(o)

18. OUTPUT:

19. DISK(Circular averaging filter):
 Code:
 f = fspecial (‘disk‘,5)
 I = imread ('tulips.jpg');
 gs = rgb2gray(i);
 o = filter2(f,gs);
 imshow(o)

20. OUTPUT:

21. GAUSSIAN(Low pass filter)
 Code:
 f = fspecial (‘gaussian‘,5)
 I = imread ('tulips.jpg');
 gs = rgb2gray(i);
 o = filter2(f,gs);
 imshow(o)

22. LAPLACIAN:
 Code:
 f = fspecial (‘laplacian‘);
 I = imread ('tulips.jpg');
 gs = rgb2gray(i);
 o = filter2(f,gs);
 imshow(o)

23. OUTPUT:

24. LOG(Laplacian of Gaussian filter):
 Code:
 f = fspecial (‘log‘,5)
 I = imread ('tulips.jpg');
 gs = rgb2gray(i);
 o = filter2(f,gs);
 imshow(o)

25. OUTPUT:

26. MOTION(Linear motion of camera):
 Code:
 f = fspecial (‘motion‘)
 I = imread ('tulips.jpg');
 gs = rgb2gray(i);
 o = filter2(f,gs);
 imshow(o)

27. OUTPUT:

28. PREWITT(Horizontal edge detection):
 Code:
 f = fspecial (‘prewitt‘)
 I = imread ('tulips.jpg');
 gs = rgb2gray(i);
 o = filter2(f,gs);
 imshow(o)

29. OUTPUT:

30. SOBEL(A Better version of prewitt):
 Code:
 f = fspecial (‘sobel‘)
 I = imread ('tulips.jpg');
 gs = rgb2gray(i);
 o = filter2(f,gs);
 imshow(o)

31. OUTPUT:

32. UNSHARP(Contrast enhancing filter):
 Code:
 f = fspecial (‘unsharp‘,5)
 I = imread ('tulips.jpg');
 gs = rgb2gray(i);
 o = filter2(f,gs);
 imshow(o/255)

33. OUTPUT: