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Image Processing
Introduction and Special Techniques

Harshit Srivastava
Department of Electrical and Electronics
Engineer...
Introduction
This presentation is an overview of some of the ideas and techniques of image
processing.
 Image processing ...
Topics
1. Image formation
2. Point processing and equalization
3. Colour correction
4. Image sampling and warping
5. Noise...
Tom and Bolt
Tom

Bolt

Tom and Bolt will be subjects of some of the imagery in this introduction.

19 March 2010

HARSHIT...
Image Formation

19 March 2010

HARSHIT SRIVASTAVA 2009-2010

5
Image Formation

projection
through lens
image of object

19 March 2010

HARSHIT SRIVASTAVA 2009-2010

6
Image Formation

projection onto
discrete sensor
array.

19 March 2010

digital camera

HARSHIT SRIVASTAVA 2009-2010

7
Image Formation

sensors register
average colour.

19 March 2010

sampled image

HARSHIT SRIVASTAVA 2009-2010

8
Image Formation

continuous colours,
discrete locations.

19 March 2010

discrete realvalued image
HARSHIT SRIVASTAVA 2009...
discrete colour output

Digital Image Formation: Quantization

continuous colours
mapped to a finite,
discrete set of colo...
Sampling and Quantization
pixel grid

real image

19 March 2010

sampled

quantized

HARSHIT SRIVASTAVA 2009-2010

sampled...
Digital Image

Colour images have 3 values
per pixel; monochrome images
have 1 value per pixel.

a grid of squares,
each o...
Colour Processing
requires some
knowledge of how
we see colors

Eye’s Light Sensors
cone density near fovea

#(blue) << #(...
Colour Sensing / Colour Perception
These are approximations
of the responses to the
visible spectrum of the
“red”, “green”...
Colour Images
• Are constructed from three
intensity maps.
• Each intensity map is pro-jected
through a colour filter
(e.g...
Colour
Images
On a
CRT

19 March 2010

HARSHIT SRIVASTAVA 2009-2010

16
Point Processing

- gamma

- brightness

original

+ brightness

+ gamma

histogram mod

- contrast

original

+ contrast
...
Colour Balance
and Saturation
Uniform changes in
colour components result
in change of tint.
E.g., if all G pixel values a...
Resampling
nearest neighbor

8

nearest neighbor

16

(resizing)
bicubic interpolation
19 March 2010

HARSHIT SRIVASTAVA 2...
ROTATION
In geometry and linear
algebra, a rotation is a
transformation in a plane
or in space that describes
the motion o...
Motion Blur
regional

vertical

original
zoom
19 March 2010

rotational

HARSHIT SRIVASTAVA 2009-2010

21
Image Warping
 Image warping is an special type of
affect which changes the function of
an image…to next level..
In imag...
Noise Reduction

blurred image

colour noise

colour-only blur

Next level of image

blurred image
19 March 2010

colour n...
Morphology
Nonlinear Processing: Binary Reconstruction
• Used after opening to grow back pieces of the original image
that...
Nonlinear Processing: Grayscale Reconstruction
original

19 March 2010

HARSHIT SRIVASTAVA 2009-2010

reconstructed openin...
Image Compression
Original image is
5244w x 4716h @
1200 ppi:
127MBytes

Yoyogi Park, Tokyo, October 1999.
19 March 2010

...
19 March 2010

File size in bytes

JPEG quality level

Image Compression: JPEG

HARSHIT SRIVASTAVA 2009-2010

27
Image Compositing
• Combine parts from separate images to form a new image.
• It’s difficult to do well.
• Requires relati...
Image Compositing Example

This man in his home office. Needs a better shirt.
19 March 2010

HARSHIT SRIVASTAVA 2009-2010
...
Image Compositing Example

NOW

This shirt demands a monogram.
19 March 2010

HARSHIT SRIVASTAVA 2009-2010

30
Image Compositing Example

And
again
some
more
changes

He needs some more color.
19 March 2010

HARSHIT SRIVASTAVA 2009-2...
Image Compositing Example

Nice. Now for the way he’d wear his hair if he had any.
19 March 2010

HARSHIT SRIVASTAVA 2009-...
Image Compositing Example

He can’t stay in the office like this.
19 March 2010

HARSHIT SRIVASTAVA 2009-2010

33
Image Compositing Example

Now the background has changed

Where’s a Daddy-O like this belong?
19 March 2010

HARSHIT SRIV...
THANK YOU
I WOULD LIKE TO THANK PROF. RICHARD ALAN PETER II

19 March 2010

HARSHIT SRIVASTAVA 2009-2010

35
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Introduction to Image Processing

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This presentation gives basic insight to image processing techniques to morphology and histogram . The presentation is made according to a novice and can be understood by anyone as it starts with basic concepts of images.

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Introduction to Image Processing

  1. 1. Image Processing Introduction and Special Techniques Harshit Srivastava Department of Electrical and Electronics Engineering Fall Semester 2010 Roll No. 0705621028 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 1
  2. 2. Introduction This presentation is an overview of some of the ideas and techniques of image processing.  Image processing is any form of signal processing for which the input is an image, such as photographs or frames of video; the output of image processing can be either an image or a set of characteristics or parameters related to the image.  Image processing usually refers to digital image processing.  Digital image processing is the use of computer algorithms to perform image processing in digital images. 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 2
  3. 3. Topics 1. Image formation 2. Point processing and equalization 3. Colour correction 4. Image sampling and warping 5. Noise reduction 6. Mathematical morphology 7. Image compression 8. Image compositing 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 3
  4. 4. Tom and Bolt Tom Bolt Tom and Bolt will be subjects of some of the imagery in this introduction. 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 4
  5. 5. Image Formation 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 5
  6. 6. Image Formation projection through lens image of object 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 6
  7. 7. Image Formation projection onto discrete sensor array. 19 March 2010 digital camera HARSHIT SRIVASTAVA 2009-2010 7
  8. 8. Image Formation sensors register average colour. 19 March 2010 sampled image HARSHIT SRIVASTAVA 2009-2010 8
  9. 9. Image Formation continuous colours, discrete locations. 19 March 2010 discrete realvalued image HARSHIT SRIVASTAVA 2009-2010 9
  10. 10. discrete colour output Digital Image Formation: Quantization continuous colours mapped to a finite, discrete set of colours. continuous colour input 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 10
  11. 11. Sampling and Quantization pixel grid real image 19 March 2010 sampled quantized HARSHIT SRIVASTAVA 2009-2010 sampled & quantized 11
  12. 12. Digital Image Colour images have 3 values per pixel; monochrome images have 1 value per pixel. a grid of squares, each of which contains a single colour each square is called a pixel (for picture element) 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 12
  13. 13. Colour Processing requires some knowledge of how we see colors Eye’s Light Sensors cone density near fovea #(blue) << #(red) < #(green) 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 13
  14. 14. Colour Sensing / Colour Perception These are approximations of the responses to the visible spectrum of the “red”, “green”, and “blue” receptors of a typical human eye. The eye has 3 types of photoreceptors: sensitive to red, green, or blue light, The simultaneous red + blue response causes us to perceive a continuous range of hues on a circle. No hue is greater than or less than any other hue. The brain transforms RGB into separate brightness and color channels (e.g., LHS). 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 14
  15. 15. Colour Images • Are constructed from three intensity maps. • Each intensity map is pro-jected through a colour filter (e.g., red, green, or blue, or cyan, magenta, or yellow) to create a monochrome image. • The intensity maps are overlaid to create a colour image. • Each pixel in a colour image is a three element vector. 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 15
  16. 16. Colour Images On a CRT 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 16
  17. 17. Point Processing - gamma - brightness original + brightness + gamma histogram mod - contrast original + contrast histogram EQ 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 17
  18. 18. Colour Balance and Saturation Uniform changes in colour components result in change of tint. E.g., if all G pixel values are multiplied by > 1 then the image takes a green cast. 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 18
  19. 19. Resampling nearest neighbor 8 nearest neighbor 16 (resizing) bicubic interpolation 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 bicubic interpolation 19
  20. 20. ROTATION In geometry and linear algebra, a rotation is a transformation in a plane or in space that describes the motion of a rigid body around a fixed point MOTION BLUR Motion blur happens when an camera cannot distinguish these values 1. Egomotion 2. Tracking 3. Optical flow 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 20
  21. 21. Motion Blur regional vertical original zoom 19 March 2010 rotational HARSHIT SRIVASTAVA 2009-2010 21
  22. 22. Image Warping  Image warping is an special type of affect which changes the function of an image…to next level.. In image warping the dimension of every side is changed to get effect.. It is an special type of affect which changes the orignality of image. 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 22
  23. 23. Noise Reduction blurred image colour noise colour-only blur Next level of image blurred image 19 March 2010 colour noise HARSHIT SRIVASTAVA 2009-2010 5x5 Wiener filter 23
  24. 24. Morphology Nonlinear Processing: Binary Reconstruction • Used after opening to grow back pieces of the original image that are connected to the opening. • Permits the removal of small regions that are disjoint from larger objects without distorting the small features of the large objects. original 19 March 2010 opened HARSHIT SRIVASTAVA 2009-2010 reconstructed 24
  25. 25. Nonlinear Processing: Grayscale Reconstruction original 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 reconstructed opening 25
  26. 26. Image Compression Original image is 5244w x 4716h @ 1200 ppi: 127MBytes Yoyogi Park, Tokyo, October 1999. 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 26
  27. 27. 19 March 2010 File size in bytes JPEG quality level Image Compression: JPEG HARSHIT SRIVASTAVA 2009-2010 27
  28. 28. Image Compositing • Combine parts from separate images to form a new image. • It’s difficult to do well. • Requires relative positions, orientations, and scales to be correct. • Lighting of objects must be consistent within the separate images. • Brightness, contrast, colour balance, and saturation must match. • Noise colour, amplitude, and patterns must be seamless. 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 28
  29. 29. Image Compositing Example This man in his home office. Needs a better shirt. 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 29
  30. 30. Image Compositing Example NOW This shirt demands a monogram. 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 30
  31. 31. Image Compositing Example And again some more changes He needs some more color. 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 31
  32. 32. Image Compositing Example Nice. Now for the way he’d wear his hair if he had any. 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 32
  33. 33. Image Compositing Example He can’t stay in the office like this. 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 33
  34. 34. Image Compositing Example Now the background has changed Where’s a Daddy-O like this belong? 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 34
  35. 35. THANK YOU I WOULD LIKE TO THANK PROF. RICHARD ALAN PETER II 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 35

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