This document provides an overview of key concepts in digital image processing. It discusses the structure of the human eye and visual perception. It also covers electromagnetic spectrum, image sensing and acquisition, image sampling and quantization, basic relationships between pixels including adjacency, regions, boundaries, edges, and distance measures. Finally, it introduces common mathematical tools used in digital image processing such as linear operations, arithmetic operations, set operations, transforms, and probabilistic methods.

1. Digital Image Processing
Chapter 2: Digital Image
Fundamentals

2. Elements of Visual Perception
 Structure
of the
human
eye

3.  Rods and cones in the retina

4.  Image formation in the eye

5.  Brightness adaptation and
discrimination

6.  Brightness discrimination

7.  Weber ratio

8.  Perceived
brightness

9.  Simultaneous contrast

10.  Optical
illusion

11. Light and the Electromagnetic
Spectrum

12.  Wavelength


c


h
E 

13. Image Sensing and Acquisition

14.  Image acquisition using a single
sensor

15.  Using
sensor
strips

16.  A simple
image
formation
model

17.  Illumination and reflectance
 Illumination and transmissivity
)
,
(
)
,
(
)
,
( y
x
r
y
x
i
y
x
f 

18. Image Sampling and Quantization

19.  Sampling
and
quantization

20.  Representing digital images

22.  Saturation and noise

23.  Number of storage bits

24.  Spatial and gray-level resolution

25.  Subsampled
and
resampled

26.  Reducing
spatial
resolution

27.  Varying
the
number
of gray
levels

28.  Varying
the
number
of gray
levels

29.  N and k in different-details images

30.  Isopreference

31.  Interpolations

32.  Zooming
and
shrinking

33. Some Basic Relationships Between
Pixels
 Neighbors of a pixel
 : 4-neighbors of p
, , ,
: four diagonal neighbors of p
, , ,
: 8-neighbors of p
and
)
(
4 p
N
)
,
1
( y
x  )
1
,
( 
y
x
)
,
1
( y
x  )
1
,
( 
y
x
)
1
,
1
( 
 y
x )
1
,
1
( 
 y
x
)
1
,
1
( 
 y
x
)
1
,
1
( 
 y
x
)
(p
ND
)
(
8 p
N
)
(
4 p
N )
(p
ND

34.  Adjacency
 : The set of gray-level values used
to define adjacency
 4-adjacency: Two pixels p and q with
values from V are 4-adjacency if q is in
the set
 8-adjacency: Two pixels p and q with
values from V are 8-adjacency if q is in
the set
V
)
(
4 p
N
)
(
8 p
N

35.  m-adjacency (mixed adjacency): Two
pixels p and q with values from V are
m-adjacency if
 q is in , or
 q is in and the set
has no pixels whose values are from V
)
(
4 p
N
)
(p
ND
)
(
)
( 4
4 q
N
p
N 

37.  Subset adjacency
 S1 and S2 are adjacent if some pixel in
S1 is adjacent to some pixel in S2
 Path
 A path from p with coordinates to
pixel q with coordinates is a
sequence of distinct pixels with
coordinates
 , ,…,
where = , = ,
and pixels and are
adjacent
)
,
( y
x
)
,
( t
s
)
,
( 0
0 y
x )
,
( 1
1 y
x )
,
( n
n y
x
)
,
( 0
0 y
x )
,
( y
x )
,
( n
n y
x )
,
( t
s
)
,
( i
i y
x )
,
( 1
1 
 i
i y
x

38.  Region
 We call R a region of the image if R is a
connected set
 Boundary
 The boundary of a region R is the set of
pixels in the region that have one or
more neighbors that are not in R
 Edge
 Pixels with derivative values that
exceed a preset threshold

39.  Distance measures
 Euclidean distance
 City-block distance
 Chessboard distance
2
1
2
2
]
)
(
)
[(
)
,
( t
y
s
x
q
p
De 



|
)
(
|
|
)
(
|
)
,
(
4 t
y
s
x
q
p
D 



|)
)
(
|
|,
)
(
max(|
)
,
(
8 t
y
s
x
q
p
D 



40. m
D
 distance: The shortest m-path
between the points

41. An Introduction to the Mathematical
Tools Used in Digital Image Processing
 Linear operation
 H is said to be a linear operator if, for
any two images f and g and any two
scalars a and b,
)
(
)
(
)
( g
bH
f
aH
bg
af
H 



42.  Arithmetic
operations
 Addition

43.  Arithmetic
operations
 Subtraction

44.  Digital
subtraction
angiography

45.  Shading correction

46.  Image multiplication

47.  Set operations

48.  Complements

49.  Logical
operations

50.  Single-
pixel
operations

51.  Neighborhood
operations

52.  Affine transformations

53.  Inverse mapping

54.  Registration

55.  Vector
operations

56.  Image transforms

57.  Fourier transform

58.  Probabilistic methods