1. Image processing (spatial &frequency domain) Image processing (spatial &frequency domain) College of Science Computer Science Department 2013-2014 E-mail: azheewria07@gmail.com University of UD

2. Computer Graphics Image process inspatial &frequency domain Faculty of Physical and Basic Education Computer Science Dep. 2014-2015 Lecturer: 14 Azhee W. MD. E-mail: azhee.muhamad@univsul.net azheewria07@gmail.com

3. Outline University of sulaimanyiah - Faculty of Physical and Basic Education - Computer Dep. 2012-2013 3  Image processing  Image processing(spatial &frequency domain)  Spatial Domain  frequency domain  Image Filtering in Spatial Domain  Linear spatial filtering  nonlinear spatial filtering  median filter  Image Filtering in Frequency Domain  Low Pass Filtering  Gaussian Low pass Filters

4. Image processing University of sulaimanyiah - Faculty of Physical and Basic Education - Computer Dep. 2014-2015 4  A technique in which the data from an image are digitized and various mathematical operations are applied to the data.  generally with a digital computer, in order to create an enhanced image that is more useful or for special purpose like security, traffic, face recoganization ).  or to perform some of the interpretation and recognition tasks usually performed by humans, also known as picture processing

5. Image processing(spatial &frequency domain) 5 The following diagram shows the image processing method in both spatial and frequency domain. LPF = Low Pass Filter (like, Ideal, Gaussian) HPF= High Pass Filter (like, Ideal, Laplacian) BPF= Band Pass Filter (like, Ideal, Stop) University of sulaimanyiah - Faculty of Physical and Basic Education - Computer Dep. 2014-2015

6. Image processing (Spatial Domain) 6  Spatial domain processing means that we are performing operations on the intensity values f(x, y) on the image. Two principle categories :  Intensity transformation (Point independent)  Spatial filtering (Point dependent)  Intensity transformation works on single pixels independent of other pixels.  Spatial filtering works on a neighborhood of every pixel. University of sulaimanyiah - Faculty of Physical and Basic Education - Computer Dep. 2014-2015

7. Image Filtering in Spatial Domain 7  The value of a pixel with coordinates (x,y) in the enhanced image is the result of performing some operation on the pixels in the neighborhood of (x,y) in the input image.  F. Spatial filtering is performed by convolving the image with a mask or a kernel. Spatial filters include sharpening, smoothing, edge detection, noise removal, etc.  In general, linear filtering of an image f of size M x N with filter size m x n is given by the expression, where g(x,y) is enhanced image, f(x,y) is input image and w(s,t) is mask or filter which is applying on input image. University of sulaimanyiah - Faculty of Physical and Basic Education - Computer Dep. 2014-2015

8. Image Filtering in Spatial Domain 8 University of sulaimanyiah - Faculty of Physical and Basic Education - Computer Dep. 2014-2015

9. Image Filtering in Spatial Domain cont’s 9 The general block diagram of image filtering in spatial domain illustrates below: University of sulaimanyiah - Faculty of Physical and Basic Education - Computer Dep. 2014-2015

10. Image Filtering in Spatial Domain 1010 1- University of sulaimanyiah - Faculty of Physical and Basic Education - Computer Dep. 2014-2015

11. Image Filtering in Spatial Domain 1111 2- University of sulaimanyiah - Faculty of Physical and Basic Education - Computer Dep. 2014-2015

12. 12 Example University of sulaimanyiah - Faculty of Physical and Basic Education - Computer Dep. 2014-2015

13. Example cont’s 1313 University of sulaimanyiah - Faculty of Physical and Basic Education - Computer Dep. 2014-2015

14. Example cont’s 1414 University of sulaimanyiah - Faculty of Physical and Basic Education - Computer Dep. 2014-2015

15. Image Filtering in Spatial Domain 1515 University of sulaimanyiah - Faculty of Physical and Basic Education - Computer Dep. 2014-2015

16. Image Filtering in Spatial Domain Median filter 1616 University of sulaimanyiah - Faculty of Physical and Basic Education - Computer Dep. 2014-2015

17. Example 1717 University of sulaimanyiah - Faculty of Physical and Basic Education - Computer Dep. 2014-2015

18. Example cont’s 1818 University of sulaimanyiah - Faculty of Physical and Basic Education - Computer Dep. 2014-2015

19. Example cont’s 1919 University of sulaimanyiah - Faculty of Physical and Basic Education - Computer Dep. 2014-2015

20. Example 20 University of sulaimanyiah - Faculty of Physical and Basic Education - Computer Dep. 2014-2015

21. Image Filtering in Frequency Domain 2121  In frequency domain, the low frequency components are generally the approximation and while high frequency components are generally details, edges, and/or noise.  One can take the discrete Fourier transform of an image, modify the Fourier transform, and take the inverse discrete Fourier transform to obtain the modified image according to the following model. University of sulaimanyiah - Faculty of Physical and Basic Education - Computer Dep. 2014-2015

22. Image Filtering in Frequency Domain 2222 The general block diagram of image filtering in frequency domain illustrates as: University of sulaimanyiah - Faculty of Physical and Basic Education - Computer Dep. 2014-2015

23. Image processing (frequency domain ) 2323  The coefficients corresponding to the new domain (frequency domain) are the transform coefficients.  Image processing operations that process transform coefficients are called transform domain processing.  Low frequency means that the sine/cosine curves are slowly varying (or even constant).  High frequency means that the sine/cosine curves are rapidly changing. University of sulaimanyiah - Faculty of Physical and Basic Education - Computer Dep. 2014-2015

24. Low‐Pass Filtering: 2424  ILPF: The Ideal Low-pass Filter is the simplest low pass filter that “cuts off” all high frequency component of the DFT that are at a certain distance from the center of the DFT. University of sulaimanyiah - Faculty of Physical and Basic Education - Computer Dep. 2014-2015

25. Gaussian Low pass Filters: 2525 The Gaussian Lowpass Filter (GLPF) with cutoff frequency at distance D0 is defined as: University of sulaimanyiah - Faculty of Physical and Basic Education - Computer Dep. 2014-2015

26. Example1 2626 Find the output of applying smoothing filter on the pixel (2,2) shown in block of image: University of sulaimanyiah - Faculty of Physical and Basic Education - Computer Dep. 2014-2015

27. Example1 2727 Solution: since the index of image width and height starts with (0,0), then the value of pixel (2,2)= 9. Now the smoothing filter must be centered on this value to change its value. Pixel(2,2)= (1/9)*(8*1+5*1+5*1+2*1+9*1+4*1+2*1+9*1+4*1)=round(48/9)=5 University of sulaimanyiah - Faculty of Physical and Basic Education - Computer Dep. 2014-2015

28. Example2 2828 Find the output of applying sharpening filter on the pixel (3,4) shown in block of image: University of sulaimanyiah - Faculty of Physical and Basic Education - Computer Dep. 2014-2015

29. Example2 29 Solution: since the index of image width and height strats with (0,0), then the value of pixel (4,3)= 3. Now the sharpening filter must be centered on this value to change its value Pixel(3,4)=(1/9)*(4*-1+4*-1+6*-1+3*-1 +3*8+5*-1+2*-1+3*-1+4*-1)  round(abs((-7/9))=1 University of sulaimanyiah - Faculty of Physical and Basic Education - Computer Dep. 2014-2015