DIP Realized by IDL

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DIP Realized by IDL

• Author Ying Li
• Course computer for imaging science

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Program Overview

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

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Zooming Module Interface
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Filter module Interface
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Fourier transform module
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Histogram module interface
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Motion blur and restoration
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Interface Architecture
Menu bar
Selection Base
Display window
Zooming base
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Zooming module
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Zooming Module

• Color table
• Keep track of the button status
• Restrict the rectangle from going outside of the
  image
• Erase old rectangles

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Color Table

• 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.

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• 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.
• The program calculated carefully to prevent the
  red rectangle from going outside of the image.

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Erase old rectangle

• 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.
• 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.

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Filter Module

• In this module I realized four kind of filters
• Ideal low pass filter
• Ideal high pass filter
• Ideal band pass filter
• Butterworth low pass filter

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Ideal low pass filter
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Ideal high pass filter
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Butterworth filter
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Butterworth FilterWe know because of the the
sharp edge of the ideal filters, there will be
some oscillation on the output signal of ideal
filters.
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This is the output of a STEP function go through
an ideal low pass filter
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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

• Here N is the order of the Butterworth filter and
  ?c is the frequency cutoff

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N2 N6
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Restore this degraded image
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Go through a ideal low pass filter
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Go through a Butterworth filter
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Fourier Transform Module
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Histogram module
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Histogram module
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Motion Blur Restoration

• Using a Inverse Filter to deconvolve the point
  spread function
• Using convolve method to get ride of the blur
  coursed by the motion of the detector or the
  object

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Inverse FilterBefore 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.
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Degradation model
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Solve by inverse filterHere if the noise
is very small and can be neglected. Then we can
restore the image by a reverse filter
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PSFThe point spread function is a line
here, if the exposure time is small enough.
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Result of inverse filter method
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Convolution Method

• 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

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Convolution methoditerate the procedure we
can get the follow equation
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Convolution method

• From that we can see that the result is the
  convolution of the derivation of the degraded
  image with a comb function

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Result of convolution method
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Result of convolution method
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Result of convolution method
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Result of convolution method
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Conclusion

• In this project I used such widgets labels,
  texts, draws,bases,drop lists, radio buttons,
  slider bars, menus, module dialogue form.
• 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

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