1) Digital image processing involves processing digital images using computer software and algorithms. It includes techniques like image enhancement, restoration, compression, and segmentation. 2) The key stages in digital image processing are image acquisition, enhancement, restoration, morphological processing, segmentation, object recognition, representation and description, compression, and color image processing. 3) Digital image processing has various applications including medical imaging, space exploration, document processing, photography, remote sensing, and video/film special effects. It covers almost the entire electromagnetic spectrum from gamma to radio waves.

1. Digital Image Processing
ELE-4707

2. PRESENTED BY
N.CH. KARTHIK.
C.S.E-B
FINAL YEAR.
BITS COLLEGE ,KMM.
Date :28-3-2012,
Kmm.

3. Dip:
“The continuum from image processing to
computer vision can be broken up into low-, mid-
and high-level processes”.
Image processing is used for two somewhat different
purposes:
• improving the visual appearance of images (pictorial
information ) to a human viewer, and
• Preparing (processing) images for measurement of the
features and structures present.
The techniques that are appropriate for each of these
tasks are not always the same, but there is
considerable overlap. This course covers methods
that are used for both purposes.

4. What Is Digital Image Processing
• The field of digital image processing refers to processing
digital images by means of a digital computer.
• A digital image can be defined as a two-dimensional
function,
f (x, y), where
x and y are spatial coordinates, and f intensity or gray level
of the image at that point.
The field of digital image processing refers to
processing digital images by means of a digital
computer.
A digital image can be defined as a two-
dimensional function,
f (x, y), where
x and y are spatial coordinates, and f
intensity or gray level of the image at that
point.

5. •Early 1920s: One of the first applications of digital imaging
was in the news-
paper industry
– The Bartlane cable picture
transmission service
– Images were transferred by submarine cable between
London and New York
– Pictures were coded for cable transfer and
reconstructed at the receiving end on a telegraph
printer

6. HISTORY:
Developed in the 1960s at the
Jet Propulsion Laboratory,
Massachusetts Institute of Technology,
Bell Laboratories, University of Maryland,
Research facilities, with application to
satellite imagery, wire-photo standards conversion,
medical imaging, videophone, character recognition,
With the fast computers
signal processors available in the 2000s,
but also the cheapest.
HISTORY:
Developed in the 1960s at the
Jet Propulsion Laboratory,
Massachusetts Institute of Technology,
Bell Laboratories, University of Maryland,
Research facilities, with application to
satellite imagery, wire-photo standards conversion,
medical imaging, videophone, character recognition,
With the fast computers
signal processors available in the 2000s,
but also the cheapest.

7. •Used in space:techngy:
– 1964: Computers used to
improve the quality of
images of the moon taken
by the Ranger 7 probe
– Such techniques were used
in other space missions
including the Apollo landings
A picture of the moon taken by the
Ranger 7 probe minutes before
landing

8. • Low-level process: (DIP)
– Primitive operations where inputs and outputs are
images Major functions: image pre-processing
like noise reduction, contrast enhancement,
image sharpening, etc.
• Mid-level process (DIP and Computer Vision and
Pattern Recognition)
– Inputs are images, outputs are attributes (e.g.,
edges). major functions: segmentation,
description, classification / recognition of objects
• High-level process (Computer Vision)
– make sense of an ensemble of recognized
objects; perform the cognitive functions normally
associated with vision

9. EXAMPLE OF DIP
•One of the most common uses of DIP
techniques: improve quality, remove noise etc

10. EXAMPLES:
(e) Poorly exposed x-ray
image
(f) The result from contrast
and edge enhancement
(g) Image blurred by motion
(h) The result of de-blurring

11. Examples: The Hubble
Telescope
•Launched in 1990 the Hubble
telescope can take images of
very distant objects
•However, an incorrect mirror
made many of Hubble’s
images useless
•Image processing
techniques were
used to fix this....

12. Finding the outline and shape of image
objects, e.g. character recognition.

13. FACE DETECTION:
Face detection

14. FACE TRACKING

15. 1)Biological Research: e.g. DNA typing and matching; automatic
counting and classification of cell structures in bone and tissue.
2) Defence and Intelligence: e.g. Reconnaissance photo-
interpretation of objects in satellite images; target acquisition and
missile guidance.
3) Document Processing: e.g. Scanning, archiving and
transmission (fax); automatic detection and recognition of printed
text (postal sorting office, tax return processing, banking cheques).
4) Law Enforcement Forensics: e.g. Photo-ID kits, criminal photo-
search, automatic fingerprint matching, DNA matching and fibre
analysis

16. 5) Photography: e.g. altering colours, zooming; adding and
subtracting objects to a scene;
6) Remote Sensing: e.g.
Land cover analysis (water, roads, cities and cultivation),
 vegetation features (water content and temperature) and crop
yield analysis;
3-D terrain rendering from satellite or aircraft data (road and
dam planning); fire and smoke detection.
7) Space exploration and Astronomy: satellite navigation and
altitude control using star positions.
8) Video and Film Special Effects: Animation,and special effects
(Star Wars).

17. EM SPECTRUM:

18. The imaging machines can cover almost the entire EM
spectrum, ranging from gamma to radio waves. These
include
• Gamma ray images
• x-ray band images
• ultra-violet band images
• visual light and infra-red images
• Imaging based on micro-waves and radio waves
•Thus, digital image processing encompasses a wide
and varied field of applications.
EM SPECTRUM:

19. Image
Restoration
Morphologica
l Processing
Segmentation
Object
Recognition
Representatio
n &
Description
Image
Compression
Colour Image
Processing
Problem Domain
Image
Acquisition
Image
Enhancement
Key Stages in Digital Image
Processing:

20. Image
Restoration
Morphologica
l Processing
Segmentation
Object
Recognition
Representatio
n &
Description
Image
Compression
Colour Image
Processing
Problem Domain
Image
Acquisition
Image
Enhancement
Key Stages in Digital Image Processing:
Image Aquisition

21. IMAGE ACQUISITION:

22. Image
Restoration
Morphologica
l Processing
Segmentation
Object
Recognition
Representatio
n &
Description
Image
Compression
Colour Image
Processing
Problem Domain
Image
Acquisition
Image
Enhancement
Key Stages in Digital Image Processing:
Image Enhancement

23. Image Enhancement:

24. Image
Restoration
Morphologica
l Processing
Segmentation
Object
Recognition
Representatio
n &
Description
Image
Compression
Colour Image
Processing
Problem Domain
Image
Acquisition
Image
Enhancement
Key Stages in Digital Image
restoration:

25. Digital Image restoration:

26. Image
Restoration
Morphologica
l Processing
Segmentation
Object
Recognition
Representatio
n &
Description
Image
Compression
Colour Image
Processing
Problem Domain
Image
Acquisition
Image
Enhancement
Key Stages in Digital Image
processing, morphological processing:

27. Digital Image Morphological Processing:

28. Image
Restoration
Morphologica
l Processing
Segmentation
Object
Recognition
Representatio
n &
Description
Image
Compression
Colour Image
Processing
Problem Domain
Image
Acquisition
Image
Enhancement
Key Stages in Digital
ImageProcessing,segmentation:

29. Digital Image Processing:
Segmentation:

30. Image
Restoration
Morphologica
l Processing
Segmentation
Object
Recognition
Representatio
n &
Description
Image
Compression
Colour Image
Processing
Problem Domain
Image
Acquisition
Image
Enhancement
Key Stages in Digital Image
Processing object recognition:

31. Digital Image Processing
Object Recognition:

32. Image
Restoration
Morphologica
l Processing
Segmentation
Object
Recognition
Representatio
n &
Description
Image
Compression
Colour Image
Processing
Problem Domain
Image
Acquisition
Image
Enhancement
Key Stages in Digital Image Processing:
Representation & Description:

33. Key Stages in Digital Image Processing:
Representation & Description:

34. Image
Restoration
Morphologica
l Processing
Segmentation
Object
Recognition
Representatio
n &
Description
Image
Compression
Colour Image
Processing
Problem Domain
Image
Acquisition
Image
Enhancement
Key Stages in Digital Image Processing,
Image Compression:

35. IMAGE COMPRESSION:

36. Image
Restoration
Morphologica
l Processing
Segmentation
Object
Recognition
Representatio
n &
Description
Image
Compression
Colour Image
Processing
Problem Domain
Image
Acquisition
Image
Enhancement
Key Stages in Digital Image Processing,
Colour Image Processing:

37. Colour Image Processing:

38. 38
DIGITAL IMAGES:
Digital images are 2D arrays (matrices) of numbers:

39. 39
SAMPLING:

40. 40
Effect of Sampling and Quantization
250 x 210 samples
256 gray levels
125 x 105
samples
50 x 42
samples
25 x 21
samples
8 gray levels 4 gray levels Binary image16 gray levels

41. IMAGE ENHANCEMENT:
the idea behind enhancement techniques is to
bring out detail that is obscured,
simply to highlight certain features of interest in
an image.
example of enhancement is when we increase the
contrast of an image because “it looks better.”

42. Image restoration:
improving the appearance of an image.
Compression:
 for reducing the storage required to save an image, or
the bandwidth required to transmit it.
Segmentation :
 partition an image into its constituent parts or objects.
In general, most difficult tasks in dip.
Representation and description :Al most always
follow the output of a segmentation stage

43. CONCLUSION:
1.Dip uses it gives effective images
2.It is used to edits image user wants type.
3.It is used in satellites ,medical, movies
e.t.c.
4.Colour images styles,animation so on…
5.User understand any thing easy way.