1. DIP Realized by IDL Author: Ying Li Course: computer for imaging science

2. Program Overview n My project has 5 modules: n 1. Zooming module n 2. Filter module n 3. Fourier transform module n 4. Histogram module n 5. Motion blur and restoration module

3. Zooming Module Interface

4. Filter module Interface

5. Fourier transform module

6. Histogram module interface

7. Motion blur and restoration

8. Interface Architecture Selection Base Menu bar Zooming base Display window

9. Zooming module

11. Zooming Module n Color table n Keep track of the button status n Restrict the rectangle from going outside of the image n Erase old rectangles

12. Color Table n I want to display a gray level image with a red region of interest displayed on it. So I need a color table of my own.

13. n I set a flag variable to keep track of the mouse buttons’ status. So user can only drag the red rectangle with the mouse button pressed down. n The program calculated carefully to prevent the red rectangle from going outside of the image.

14. Erase old rectangle n In order for the red rectangle to go with the mouse, the program must erase the old rectangle and draw a new rectangle at the new position. n To do this I use a hidden draw widget to display the image at exactly the same position, and erase old rectangle by copy data from the hidden window.

15. Filter Module n In this module I realized four kind of filters: n Ideal low pass filter n Ideal high pass filter n Ideal band pass filter n Butterworth low pass filter

17. Ideal low pass filter

18. Ideal high pass filter

19. Butterworth filter

20. Butterworth Filter: We know because of the the sharp edge of the ideal filters, there will be some oscillation on the output signal of ideal filters.

21. This is the output of a STEP function go through an ideal low pass filter

22. So, we want a kind of filter whose edges go down slowly. Butterworth filter was introduced. This is the equation of a 1-D Butterworth filter: n Here N is the order of the Butterworth filter and  c is the frequency cutoff

23. N=2 N=6

24. Restore this degraded image:

25. Go through a ideal low pass filter

26. Go through a Butterworth filter

27. Fourier Transform Module

28. Histogram module

30. Motion Blur Restoration n Using a Inverse Filter to deconvolve the point spread function n Using convolve method to get ride of the blur coursed by the motion of the detector or the object

31. Inverse Filter Before image restoration can be accomplished, the PSF of the blurring function(that is the system transfer function of the degrading system) must be known. Actually most system that course the degrading of images are linear shift invariant system.

32. Degradation model:

33. Solve by inverse filter: Here if the noise is very small and can be neglected. Then we can restore the image by a reverse filter:

34. PSF: The point spread function is a line here, if the exposure time is small enough.

35. Result of inverse filter method:

36. Convolution Method: n We still have some other ways to restore a motion blurred image. The motion blur is coursed by the moving of the detector or the object within the exposure thim T. That is:

37. Convolution method: iterate the procedure we can get the follow equation:

38. Convolution method: n From that we can see that the result is the convolution of the derivation of the degraded image with a comb function

39. Result of convolution method

43. Conclusion: n In this project I used such widgets: labels, texts, draws,bases,drop lists, radio buttons, slider bars, menus, module dialogue form. n I realized such functions: Region of interest, ideal low pass filter, ideal high pass filter, ideal band pass filter, butterworth filter, with different parameters, fourier transform, image histogram, histogram equalization, image blur, a inverse filter, convolution method to restore motion blur, a module dialogue form