1. image processing
processing digital images by means of a digital
computer.
This includes:
Analyze the image
Manipulate it
Store it
Display it

2. Example of image processing
cropping an image:
Create new image from part of original image.
Image Resizing:
shrink / expand an image from its original size.

3. Example of image processing-cont
Image Negative:
A negative image is a total inversion of a positive image,
in which light areas appear dark and vice versa.
Contrast Stretching:
Contrast stretching is a simple image enhancement
technique that attempts to improve the contrast in an
image by `stretching' the range of intensity values it
contains to span a desired range of values

4. Applications of image processing
Television
Signal Processing
Satellite Image Processing
Medical Image Processing
Robot Control
Visual Communications
LawEnforcement

5. Examples of applications
Optical Character Recognition (OCR):
Handwritten: sorting letters
Printed texts: reading machines
Biometrics:
Finger prints recognition.
Speech recognition.

6. Examples of applications-cont
Diagnostic systems :
Medical diagnosis: X-Ray, EKG analysis.
Machine diagnostics, waster detection
Military applications:
Automated Target Recognition (ATR).
Image segmentation and analysis.

7. Areas in image processing
Image acquisition :
capturing an image in digital form
Image enhancement :
making an image look better in a subjective way.

8. Areas in image processing-cont
Image restoration :
improving the appearance of any image objectively.
Image segmentation :
partitioning an image into its constituent parts or
objects.

9. Areas in image processing-cont
Image compression :
reducing the stored and transmitted image data.
Representation and description :
boundary representation vs. region representation.
Boundary descriptors vs. region descriptors.

10. Areas in image processing-cont
Recognition :
Identifying an object based on its features and descriptors

11. Image Restoration
estimated degradation, and restoring it to its original
appearance.
used in photography or publishing where an image.

12. Figure of Image Restoration

13. Image noise
meaning of "noise” is "unwanted sound”
Image noise is random variation of brightness or color
information in image.

14. Type of noise
Gaussian:
give no overshoot to a step function input while
minimizing the rise and fall time.
Impulse:
dynamic system is its output when presented with a
brief input signal.

15. example for Gaussian Noise
Original Image Corrupted Image

16. IMPULSE NOISE
Impulse noise is very common in digital images.
Impulse noise is always independent and uncorrelated to the
image pixels.
unlike Gaussian noise, for an impulse noise corrupted image all the
image pixels are not noisy.

17. models of impulse noise
Salt impulse noise:
assumed to have the brightest gray level. Appears as
white spot in the image
Pepper impulse noise:
darkest value of the gray level in the image. Appears as
black spot in the image

18. Example for Impulse Noise
Original Image Corrupted Image

19. Example for salt and pepper Noise and
it’s Restoration by Justin et. el Filter

20. Filtering Techniques for Restoration
Mean Filter:
Mean filter or average filter is windowed filter to linear
class that smoothes signal(image).
Median Filter:
Nonlinear digital filtering technique, used to remove
noise.

21. example
Mean Filter:
Median Filter:

23. Proposed work
To Implement the following research papers from IEEE in
Matlab and analyze it’s performance using various criteria
1. SrinivasanK.S and Ebenezer.D, “A new fast and efficient
Decision – Based algorithm for removal of high - density
impulse noises”,EEE signal processing letters, vol.14, no.3, 2007
2. Madhu S. Nair, Revathy.K and Rao Tatavarti, “An improved
decision – based algorithm for impulse noise removal”,
Congress on image and signal processing, 2008
3. Justin Varghese ”An Effective Filter for the Restoration of
Highly Corrupted Digital Images” IEEE World Congress on
Nature & Biologically Inspired Computing, 2009. NaBIC 2009.
Publication Year: 2009 , Page(s): 1480 - 1485

24. conclusion
proposed software works for the restoration of
images corrupted with almost all impulse noise levels.
It will produce patches free outputs from images
corrupted by higher levels of impulse noise.
Experimental analysis will be done to analyze the
performance of the filters.