under aegis of board of studies electronics, sppu, …digital image and video processing (404184)...
TRANSCRIPT
Digital Image and Video Processing
(404184)
“FACULTY ORIENTATION WORKSHOP ON BE
REVISED SYLLABUS 2015 COURSE”
UNDER AEGIS OF BOARD OF STUDIES
ELECTRONICS, SPPU, PUNE
Presenter:
Mrs.Priya Charles Head E&TC
DYPIEMR,Pune
Structure
Unit I
Unit I difference
Unit I : Fundamentals of Image Processing (6L)(old)
Steps in image processing, Human visual system, Sampling & quantization, Representing digital images, Spatial & gray-level resolution, Image file formats, Basic relationships between pixels, Distance Measures. Basic operations on images-image addition, subtraction, logical operations, scaling, translation, rotation. Image Histogram. Color fundamentals & models – RGB, HSI ,YIQ.
Unit I : Fundamentals of Image Processing (5 Hrs)(new)
Steps in Image processing, Human visual system, Sampling & quantization, Representing digital images, spatial and gray level resolution, Image file formats, Basic relationships between pixels, Distance Measures, Basic operations on images image addition, subtraction, logical operations, scaling translation, rotation. Color fundamentals and models RGB, HIS, YIQ
1. Understand the fundamental concepts of Digital Image
Processing with basic relationship of pixels and mathematical
operations on 2-D data.
2. Learn design and integrate image enhancement and image
restoration techniques
3. Understand object segmentation and image analysis
techniques
4. Learn the need for effective use of resources such as
storage and bandwidth and ways to provide effective use of
them by data compression techniques
5. Learn basic concepts of video processing
Teaching Scheme: Lecture : 03 hrs/week
Course Objectives:
Course Outcomes
On completion of the course, student will be able to
1) Develop and implement basic mathematical operations on digital images.
2) Analyze and solve image enhancement and image restoration problems.
3) Identify and design image processing techniques for object segmentation and recognition.
4) Represent objects and region of the image with appropriate method.
5) Apply 2-D data compression techniques for digital images.
6) Explore video signal representation and different algorithm for video processing.
Practical
(Perform any 8 practical on appropriate software)
1. Perform basic operations on images.
2. Perform conversion between color spaces.
3. Perform histogram equalization.
4. Perform image filtering in spatial domain.
5. Perform image filtering in frequency domain.
6. Perform image restoration.
7. Perform image compression using DCT / Wavelet transform.
8. Perform edge detection using various masks.
9. Perform global and adaptive thresholding.
10. Apply morphological operators on an image.
11. Obtain boundary / regional descriptors of an image.
12. Extraction of frames from video, improve the quality and convert them back to compressed video.
Books
Text books:
Rafael C. Gonzalez and Richard E. Woods, “Digital Image
Processing”, Third Edition, - Pearson Education
Jain E G Richardson H.264 and MPEG
Video Compression: Video Coding for Next Publication, 3rd
Edition.
Reference Books:
1. A. K. Jain, Fundamentals of digital image processing, Prentice
Hall of India, 1989.
2. Pratt William K. "Digital Image Processing", John Wiley &
sons
3. A. Bovik, Handbook of Image & Video Processing, Academic
Press, 2000
Syllabus Mapping with Book
Sr. No. Contents Mapping
1 Steps in Image processing(1) T1- Chapter No. 1(25-28)
2 Human visual system(15 mins) T1- Chapter No. 2 (36-44)
3 Sampling & quantization, Representing digital images, spatial
and gray level resolution(1 hr)
T1- Chapter No. 2(52-65
4 Image file formats(15 mins) Additional:Pg-61
5 Basic relationships between pixels, Distance Measures(1 hr) T1- Chapter No. 2(68-72)
6 Basic operations on images image addition, subtraction, logical
operations, scaling translation, rotation.(1 hr)
T1- Chapter No. 2(72-95)
7 Color fundamentals and models RGB, HIS, YIQ(1/2 hr)
T1- Chapter No. 6(394-413)
T1:Rafael C. Gonzalez and Richard E. Woods, “Digital Image Processing”, Third
Edition, - Pearson Education
Additional:S Sridhar, “Digital Image Processing”, Oxford University Press
9
5 hrs
Digital image processing is the study of representation and
manipulation of pictorial information by a computer.
Improve pictorial information for better clarity (human
interpretation)
Examples:
1 Enhancing the edges of an image to make it appear
sharper
2 Remove “noise” from an image
3 Remove motion blur from an image
Introduction and Digital Image
Fundamentals
History of Digital Image
Processing
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
Early digital image
History of DIP
Mid to late 1920s: Improvements to the
Bartlane system resulted in higher quality images
New reproduction
processes based
on photographic
techniques
Increased number
of tones in
reproduced images Improved
digital
image Early 15 tone digital
image
History of DIP
1960s: Improvements in computing technology and the
onset of the space race led to a surge of work in digital image
processing
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
History of DIP
1970s: Digital image processing begins to be used in
medical applications
1979: Sir Godfrey N.
Hounsfield & Prof. Allan M.
Cormack share the Nobel
Prize in medicine for the
invention of tomography,
the technology behind
Computerised Axial
Tomography (CAT) scans
Typical head slice CAT
image
History of DIP
1980s - Today: The use of digital image processing
techniques has exploded and they are now used for
all kinds of tasks in all kinds of areas
Image enhancement/restoration
Artistic effects
Medical visualisation
Industrial inspection
Law enforcement
Human computer interfaces
Examples: Image
Enhancement
One of the most common uses of DIP techniques: improve quality,
remove noise etc
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
Examples: Artistic Effects
Artistic effects are used to
make images more visually
appealing, to add special
effects and to make
composite images
Examples: Medicine
Take slice from MRI scan of canine heart, and find boundaries between
types of tissue
Image with gray levels representing tissue density
Use a suitable filter to highlight edges
Original MRI Image of a Dog Heart Edge Detection Image
Examples: GIS
Geographic Information Systems
Digital image processing techniques are used extensively to manipulate
satellite imagery
Terrain classification
Meteorology
Examples: GIS
Night-Time Lights of the
World data set
Global inventory of human
settlement
Not hard to imagine the
kind of analysis that might
be done using this data
Examples: Industrial Inspection
Human operators are expensive,
slow and unreliable
Make machines do the job
instead
Industrial vision systems are
used in all kinds of industries
Examples: PCB Inspection
Printed Circuit Board (PCB) inspection
Machine inspection is used to determine that all components are
present and that all solder joints are acceptable
Both conventional imaging and x-ray imaging are used
Examples: Law Enforcement
Image processing techniques are
used extensively by law enforcers
Number plate recognition for speed
cameras/automated toll systems
Fingerprint recognition
Enhancement of CCTV images
Examples: HCI
Try to make human computer interfaces
more natural
Face recognition
Gesture recognition
These tasks can be extremely difficult
Visual Perception: Human Eye
(Picture from Microsoft Encarta 2000)
1. The lens contains 60-70% water, 6% of fat.
2. The iris diaphragm controls amount of light that enters the eye.
3. Light receptors in the retina
- About 6-7 millions cones for bright light vision called photopic
- Density of cones is about 150,000 elements/mm2.
- Cones involve in color vision. - Cones are concentrated in fovea about 1.5x1.5 mm2.
- About 75-150 millions rods for dim light vision called scotopic
- Rods are sensitive to low level of light and are not involved
color vision.
4. Blind spot is the region of emergence of the optic nerve from the eye.
Visual Perception: Human Eye (cont.)
Distribution of Rods and Cones in the Retina
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.
Image Formation in the Human Eye
(Picture from Microsoft Encarta 2000)
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.
Position
Inte
nsi
ty
Brightness Adaptation of Human Eye : Mach Band Effect
Mach Band Effect
Intensities of surrounding points
effect perceived brightness at each
point.
In this image, edges between bars
appear brighter on the right side
and darker on the left side.
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.
In area A, brightness perceived is darker while in area B is
brighter. This phenomenon is called Mach Band Effect.
Position
Inte
nsi
ty
A B
Mach Band Effect (Cont)
Mind Map Exercise: Mind Mapping For Note Taking
Beau Lotto: Optical Illusions Show How We See
http://www.ted.com/talks/lang/eng/beau_lotto_optical_illusions_show_how_we_see.html
Image “After snow storm”
Fundamentals of Digital Images
f(x,y)
x
y
w An image: a multidimensional function of spatial coordinates.
w Spatial coordinate: (x,y) for 2D case such as photograph,
(x,y,t) for movies
w The function f may represent intensity (for monochrome images)
or color (for color images) or other associated values.
Origin
Digital Image Representation
Key steps in Digital Image Processing
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Object
Recognition
Image
Enhancement
Representatio
n &
Description
Problem Domain
Color Image
Processing
Image
Compression
Knowledge Base
Key Stages in Digital Image Processing: Image Acquisition
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Object
Recognition
Image
Enhancement
Representatio
n &
Description
Problem Domain
Color Image
Processing
Image
Compression
Key Stages in Digital Image Processing:
Image Enhancement
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representatio
n &
Description
Image
Enhancement
Object
Recognition Problem Domain
Color Image
Processing
Image
Compression
Key Stages in Digital Image Processing: Image Restoration
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Object
Recognition
Image
Enhancement
Representatio
n &
Description
Problem Domain
Color Image
Processing
Image
Compression
Key Stages in Digital Image Processing: Morphological Processing
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representatio
n &
Description
Image
Enhancement
Object
Recognition
Problem Domain
Color Image
Processing
Image
Compression
Key Stages in Digital Image Processing: Segmentation
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Object
Recognition
Image
Enhancement
Representatio
n &
Description
Problem Domain
Color Image
Processing
Image
Compression
Key Stages in Digital Image Processing: Representation & Description
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation Image
Enhancement
Problem Domain
Color Image
Processing
Image
Compression
Representation
& Description
Object
Recognition
Key Stages in Digital Image Processing: Object Recognition
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Object
Recognition
Image
Enhancement
Representation
&
Description
Problem Domain
Color Image
Processing
Image
Compression
Key Stages in Digital Image Processing: Image Compression
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Object
Recognition
Image
Enhancement
Representation
&
Description
Problem Domain
Color Image
Processing
Image
Compression
Key Stages in Digital Image Processing: Colour Image Processing
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Object
Recognition
Image
Enhancement
Representation
&
Description
Problem Domain
Color Image
Processing
Image
Compression
Digital Image Types : Intensity Image
Intensity image or monochrome image
each pixel corresponds to light intensity
normally represented in gray scale (gray
level).
39871532
22132515
372669
28161010
Gray scale values
39871532
22132515
372669
28161010
39656554
42475421
67965432
43567065
99876532
92438585
67969060
78567099
Digital Image Types : RGB Image
Color image or RGB image:
each pixel contains a vector
representing red, green and
blue components.
RGB components
Image Types : Binary Image
Binary image or black and white image
Each pixel contains one bit :
1 represent white
0 represents black
1111
1111
0000
0000
Binary data
Gray Level and Color Images
A Gray Level Image is a Matrix
f(0,0) f(0,1) f(0,2) …. …. f(0,n-1)
f(1,0) f(1,1) f(1,2) …. …. f(1,n-1)
. . .
. . .
. . .
f(m-1,0) f(m-1,1) f(m-1,2) … …. f(m-1,n-1)
An image of m rows, n columns, f(i,j) is in [0,255]
Gray and Color Image Data
0, 64, 144, 196,
225, 169, 100, 36
(R, G, B) for a color pixel
Red – (255, 0, 0)
Green – ( 0, 255, 0)
Blue – ( 0, 0, 255)
Cyan – ( 0,255, 255)
Magenta – (255, 0, 255)
Yellow – (255, 255, 0)
Gray – (128, 128, 128)
How to choose the spatial resolution
= Sampling locations
Ori
gin
al i
mag
e S
ample
d i
mag
e
Under sampling, we lost some image details!
Spatial resolution
How to choose the spatial resolution : Nyquist Rate O
rigin
al i
mag
e
= Sampling locations
Minimum
Period Spatial resolution
(sampling rate)
Sampled image
No detail is lost!
Nyquist Rate:
Spatial resolution must be less or equal
half of the minimum period of the image
or sampling frequency must be greater or
Equal twice of the maximum frequency.
2mm
1m
m
Effect of Spatial Resolution
256x256 pixels
64x64 pixels
128x128 pixels
32x32 pixels
Effect of Spatial Resolution
Effect of Spatial Resolution
Can we increase spatial resolution by interpolation ?
Down sampling is an irreversible process.
Image Quantization
Image quantization:
discretize continuous pixel values into discrete numbers
Color resolution/ color depth/ levels:
- No. of colors or gray levels or
- No. of bits representing each pixel value
- No. of colors or gray levels Nc is given by
b
cN 2
where b = no. of bits
Quantization function
Light intensity
Qu
anti
zati
on l
evel
0
1
2
Nc-1
Nc-2
Darkest Brightest
Intensity Level Resolution
Intensity level resolution refers to the number of intensity levels used to
represent the image
The more intensity levels used, the finer the level of
detail discernable in an image
Intensity level resolution is usually given in terms of
the number of bits used to store each intensity level
Number of Bits Number of Intensity
Levels Examples
1 2 0, 1
2 4 00, 01, 10, 11
4 16 0000, 0101, 1111
8 256 00110011, 01010101
16 65,536 1010101010101010
Effect of Quantization Levels
256 levels 128 levels
32 levels 64 levels
Effect of Quantization Levels (cont.)
16 levels 8 levels
2 levels 4 levels
In this image,
it is easy to see
false contour.
Zooming and
shrinking
Common image file formats
PGM (Portable Gray Map)
Bit Map File
PNG (Portable Network Graphics)
GIF (Graphic Interchange Format) –
JPEG (Joint Photographic Experts Group)
TIFF (Tagged Image File Format)
FITS (Flexible Image Transport System)
Basic Relationship of Pixels
x
y
(0,0)
Conventional indexing method
(x,y) (x+1,y) (x-1,y)
(x,y-1)
(x,y+1)
(x+1,y-1) (x-1,y-1)
(x-1,y+1) (x+1,y+1)
Neighbors of a Pixel
p (x+1,y) (x-1,y)
(x,y-1)
(x,y+1)
4-neighbors of p:
N4(p) =
(x-1,y)
(x+1,y)
(x,y-1)
(x,y+1)
Neighborhood relation is used
to tell adjacent pixels. It is
useful for analyzing regions.
Note: q N4(p) implies p N4(q)
4-neighborhood relation considers only vertical and
horizontal neighbors.
p
(x+1,y-1) (x-1,y-1)
(x-1,y+1) (x+1,y+1)
Diagonal neighbors of p:
ND(p) =
(x-1,y-1)
(x+1,y-1)
(x-1,y+1)
(x+1,y+1)
Neighbors of a Pixel (cont.)
Diagonal -neighborhood relation considers only diagonal
neighbor pixels.
p (x+1,y) (x-1,y)
(x,y-1)
(x,y+1)
(x+1,y-1) (x-1,y-1)
(x-1,y+1) (x+1,y+1)
Neighbors of a Pixel (cont.)
8-neighbors of p:N8(p)=N4(p) U
ND(p)
(x-1,y-1)
(x,y-1)
(x+1,y-1)
(x-1,y)
(x+1,y)
(x-1,y+1)
(x,y+1)
(x+1,y+1)
N8(p) =
8-neighborhood relation considers all neighbor pixels.
Connectivity
Connectivity is an important concept to find the region
property of an image or the property of a particular region within
the image.
It is used for
Establishing object boundaries
Defining image components/regions etc
For p and q from the same class
w 4-connectivity: p and q are 4-connected p,q Î V & q Î N4(p)
w 8-connectivity: p and q are 8-connected p,q Î V & q Î N8(p)
w mixed-connectivity (m-connectivity):
p and q are m-connected if q Î N4(p) or
q Î ND(p) and N4(p) Ç N4(q) = Æ
Either q has to be a 4 neighbor of p or p has to be a 4 neighbor of q
Or q has to be a diagonal neighbor of p, but at the same time N4 (p) intersection with
N4(q) must be equal to Æ
N4(p) Ç N4(q)
this indicates the set of points which are 4 neighbors of both the
points p and q
If the point q belongs to the diagonal neighbor of p and there is a
common set of points which have 4 neighbors to both the points p
and q then M connectivity is not valid
Ex: V={1}
0 1 1
0 1
0 1
0 1 1
0 1
0 0 1
0 1 1
0 1 0
0 1
4 connected 8 connected M connected
In case of M connectivity the two points are M
connected if one is the 4 neighbor of the other,
Or
one is the 4 neighbor of the other and at the same time
they don’t have any common neighbor.
Path (cont.)
p
q
p
q
p
q
8-path from p to q
results in some ambiguity
m-path from p to q
solves this ambiguity
8-path m-path
Distance
For pixel p, q, and z with coordinates (x,y), (s,t) and (u,v),
D is a distance function or metric if
w D(p,q) 0 (D(p,q) = 0 if and only if p = q)
w D(p,q) = D(q,p)
w D(p,z) D(p,q) + D(q,z)
Example: Euclidean distance
22 )()(),( tysxqpDe -+-
Distance (cont.)
D4-distance (city-block distance) is defined as
tysxqpD -+-),(4
1 2
1 0
1 2
1
2
2
2
2
2
2
Pixels with D4(p) = 1 is 4-neighbors of p.
Distance (cont.)
D8-distance (chessboard distance) is defined as
),max(),(8 tysxqpD --
1
2
1 0
1
2
1
2
2
2
2
2
2
Pixels with D8(p) = 1 is 8-neighbors of p.
2 2
2
2
2
2 2 2
1
1
1
1
Basic operations on images
Arithmetic
Logical
Geometric
Addition
Subtraction
Multiplication
division
Brightening an
image
detecting the missing
components to mask the image for
obtaining region of interest
decrease the brightness
of the image
AND
OR
NOT
XOR
To isolate the interested
region from rest of the
image
Negative of image Detect change in images
translation
Rotation
scaling
Arithmetic and Logic Operations
a b
NOT(a)
a . b
a + b
Basic arithmetic operations on images
Arithmetic and Logic Operations
. =
+ =
Image Subtraction
(a) original fractal image.
(b) Result of setting the
four lower-order bit planes to zero. (c)
Difference between (a)
and (b) . (d) Histogram
equalized difference
image.
a b
c d
© 2002 R. C. Gonzalez & R. E. Woods
Colour Fundamentals
In 1666 Sir Isaac Newton discovered that when a beam of sunlight passes through
a glass prism, the emerging beam is split into a spectrum of colours
Color Spectrum
Band of visible light is relatively narrow in the band of frequencies
in the electromagnetic spectrum.
Perception (Cont.)
Primary Colors
The cone cells in human eye can be divided into
three categories, corresponding roughly to red, green
and blue (Figure 6.3).
Due to these characteristics of the human eye, colors
are seen as variable combinations of the primary
colors red (700 nm), green (546.1 nm), and blue
(435.8 nm).
Standardized in 1931.
This standardization does not mean these three primary
colors can generate all spectrum colors.
Secondary Colors
The primary colors can be
added to produce the
secondary colors of light:
Cyan, Magenta, Yellow.
The primary colors of
pigments are cyan, magenta,
and yellow, while the
secondary colors are red,
green, and blue.
More Fundamentals
The characteristics generally used to distinguish one color from another are hue, saturation, and brightness.
Hue: associated with color as perceived by an observer.
Saturation: relative purity or the amount of white light mixed with a hue.
Brightness: intensity of light.
Hue and saturation are taken together are called chromaticity; therefore, a color can be characterized by its chromaticity and brightness.
CIE Chromacity Diagram (cont…)
Green: 62% green, 25%
red and 13% blue
Red: 32% green, 67% red
and 1% blue
Colour Models
From the previous discussion it should be obvious that there
are different ways to model colour
We will consider two very popular models used in colour
image processing:
RGB (Red Green Blue)
HIS (Hue Saturation Intensity)
Converting From RGB To HSI
Given a colour as R, G, and B its H, S, and I values are calculated as
follows:
H if B G
360- if B G
cos-1
12R -G + R - B
R -G 2+ R - B G - B
12
S 1-3
R+G + B min R,G,B
I 13R+G+ B
Converting From HSI To RGB
Given a colour as H, S, and I it’s R, G, and B values are calculated as follows:
RG sector (0 <= H < 120°)
GB sector (120° <= H < 240°)
G 3I - R+ B
B I 1- S
R I 1+ScosH
cos 60-H
B 3I - R+G
R I 1- S
G I 1+Scos H -120 cos H -60
Converting From HSI To RGB (cont…)
BR sector (240° <= H <= 360°)
R 3I - G+ B
G I 1- S
B I 1+Scos H -240 cos H -180
RGB -> HSI -> RGB
RGB
Image
Saturation
Hue
Intensity
RGB -> HSI -> RGB (cont…)
Hue
Intensity
Saturation
RGB
Image
Questions
1. Explain components of image processing system with neat diagram.[8]
2. Define MTF. Explain it for the Human Vision[8]
3. Explain with neat diagrams the various mechanisms for image acquisition[8]
4. Explain the following in context of human vision[8]
1. Luminance & Brightness
2. MTF
5. With the help of neat diagram explain various steps in image processing [8]
6. Explain the concept of Image sampling and quantization with suitable sketch[8]
7. Explain the following with respect to digital image.
1. Spatial and gray level resolution
2. Profile and standard deviation
Unit II
Unit II difference
Unit II: Image Enhancement and Restoration (6L)(old)
Spatial domain enhancement: Point operations-Log transformation, Power-law
transformation, Piecewise linear transformations, Histogram equalization. Filtering
operations- Image smoothing, Image sharpening. Frequency domain enhancement:
2D DFT, Smoothing and Sharpening in frequency domain, Homomorphic filtering.
Restoration: Noise models, Restoration using Inverse filtering and Wiener filtering
Unit II : Fundamentals of Image Processing (8 Hrs)(new)
Point Log transformation, Power law transformation, Piecewise linear transformation,
Image histogram, histogram equalization, Mask processing of images, filtering
operations- Image smoothing, image sharpening, frequency domains image
enhancement: 2D DFT, smoothing and sharpening in frequency domain, Pseudo
coloring.
Image Restoration: Noise models, restoration using Inverse filtering and Wiener
filtering
Syllabus Mapping with Book
Sr. No. Contents Mapping
1 Point Log transformation, Power law transformation, Piecewise
linear transformation,,.
T1- Chapter No. 3(104-119)
2 Image histogram, histogram equalization T1- Chapter No. 3(122-144)
R1-pg-241
3 Mask processing of images, filtering operations- Image
smoothing, image sharpening, frequency domains image
enhancement: 2D DFT, smoothing and sharpening in frequency
domain
T1- Chapter No. 3(144-
167),chapter 4-(pgs 220-242)
Chapter-4(255-288)
4 Pseudo coloring R1-chapter 7 Pg-6262
5 Image Restoration: Noise models, restoration using Inverse
filtering and Wiener filtering
T1- Chapter No. 5(311-356)
T1:Rafael C. Gonzalez and Richard E. Woods, “Digital Image Processing”, Third
Edition, - Pearson Education
R1A. K. Jain, Fundamentals of digital image processing, Prentice Hall of India,
1989.
99
8 hrs
What Is Image Enhancement?
Image enhancement is the process of making
images more useful
The reasons for doing this include:
– Highlighting interesting detail in images
– Removing noise from images
– Making images more visually appealing
Image Enhancement Examples
Image Enhancement Examples
Spatial & Frequency Domains
There are two broad categories of image
enhancement techniques
– Spatial domain techniques
• Direct manipulation of image pixels
– Frequency domain techniques
• Manipulation of Fourier transform or wavelet transform
of an image
Conten
ts – What is point processing?
– Negative images
– Thresholding
– Logarithmic transformation
– Power law transforms
– Grey level slicing
– Bit plane slicing
Basic Spatial Domain Image Enhancement
Origin x
y Image f (x, y)
(x, y)
•Most spatial domain enhancement operations can
be reduced to the form
•g (x, y) = T[ f (x, y)]
•where f (x, y) is the
input image, g (x, y) is
the processed image and
T is some operator
defined over some
neighbourhood of (x, y)
Point Processing
•The simplest spatial domain operations occur when the neighbourhood is simply the pixel itself
•In this case T is referred to as a grey level transformation function or a point processing operation
•Point processing operations take the form
•s = T ( r )
•where s refers to the processed image pixel value
and r refers to the original image pixel value
Basic Grey Level Transformations
•There are many different kinds of grey level transformations
1)Linear
• Negative/Identity
2)Logarithmic • Log/Inverse log
3)Power law • nth power/nth root
Point Processing
Example: Negative
Images (cont…) Original Image x
y Image f (x, y)
Enhanced Image x
y Image f (x, y)
s = intensitymax - r
Piecewise Linear Transformation
Functions
Case 1: Contrast Stretching
Case 2:Gray-level Slicing
Case 3:Bit-plane Slicing
Image Histograms
The histogram of an image shows us the
distribution of grey levels in the image
Massively useful in image processing, especially in
segmentation
Grey Levels
Fre
quenci
es
Histogram Examples (cont…)
Histogram Examples (cont…)
Histogram Examples (cont…)
Histogram Examples (cont…)
Histogram Equalisation
Spreading out the frequencies in an image (or equalising the image) is a simple way to improve dark or washed out images
The formula for histogram equalisation is given where
– rk: input intensity
– sk: processed intensity
– k: the intensity range (e.g 0.0 – 1.0)
– nj: the frequency of intensity j
– n: the sum of all frequencies
sk T (rk ) k
j 1
pr (rj )
k j
n
n
j 1
The mode is the value that occurs most frequently in a distribution and is usually the highest point on the curve (histogram). It is common, however, to encounter more than one mode in a remote sensing dataset.
The median is the value midway in the frequency distribution. One-half of the area below the distribution curve is to the right of the median, and one-half is to the left
The mean is the arithmetic average and is defined as the sum of all brightness value observations divided by the number of observations.
Example
96
2 3 3 2
4 2 4 3
3 2 3 5
2 4 2 4
4x4 image
Gray scale = [0,9]
6
5
4
3
2
1
No. of pixels
Gray level
0 1 2 3 4 5 6 7 8 9
histogram
Gray
Level(j)
0
1
2
3
4
5
6
7
8
9
No. of pixels
0
0
6
5
4
1
0
0
0
0
k
n j j 0
0
0
6
11
15
16
16
16
16
16
s n j
k 0
0
6 /
11
/
15
/
16
/
16
/
16
/
16
/
16
/
j 0 n 16 16 16 16 16 16 16 16
s x 9
0
0
3.3
3
6.1
6
8.4
8
9
9
9
9
9
Exampl
e
Example
98
3 6 6 3
8 3 8 6
6 3 6 9
3 8 3 8
Gray scale = [0,9]
Histogram equalization
No. of pixels
6
5
4
3
2
1
Output image
0 1 2 3 4 5 6 7 8 9 Gray level
Simple Neighbourhood Operations
Example
164 170 175 162 173 151
Original Image
123 127 128 119 115 130
140 145 148 153 167 172
133 154 183 192 194 191
194 199 207 210 198 195
x
y
Enhanced Image x
y
The Spatial Filtering Process
r s t
u v w
x y z
Origin x
y Image f (x, y)
eprocessed = v*e +
r*a + s*b + t*c +
u*d + w*f +
x*g + y*h + z*i
Filter Simple 3*3
Neighbourhood e 3*3 Filter
a b c
d e f
g h i
Original Image
Pixels
*
The above is repeated for every pixel in the original
image to generate the filtered image
Smoothing Spatial Filters
•One of the simplest spatial filtering operations we can perform is a smoothing operation
– Simply average all of the pixels in a neighbourhood around a central value
– Especially useful in removing noise from images
– Also useful for highlighting gross detail
1/9 1/9
1/9
1/9 1/9
1/9
1/9 1/9
1/9
Simple
averaging filter
Smoothing Spatial
Filtering 1/9
1/9 1/9
1/9 1/9
1/9
1/9 1/9
1/9
Origin x
y Image f (x, y)
e = 1/9*106 + 1/9*104 + 1/9*100 + 1/9*108 + 1/9*99 + 1/9*98 + 1/9*95 + 1/9*90 + 1/9*85
Filter Simple 3*3
Neighbourhood 106
104
99
95
100 108
98
90 85
1/9 1/9
1/9
1/9 1/9
1/9
1/9 1/9
1/9
3*3 Smoothing
Filter
104 100 108
99 106 98
95 90 85
Original Image
Pixels
*
= 98.3333
The above is repeated for every pixel in the original
image to generate the smoothed image
Weighted Smoothing Filters
•More effective smoothing filters can be
generated by allowing different pixels in the
neighbourhood different weights in the
averaging function
– Pixels closer to the
central pixel are more
important
– Often referred to as a
weighted averaging
1/16 2/16
1/16
2/16 4/16
2/16
1/16 2/16
1/16
Weighted
averaging filter
Sharpening Spatial Filters
Previously we have looked at smoothing filters
which remove fine detail
Sharpening spatial filters seek to highlight fine
detail
– Remove blurring from images
– Highlight edges
Sharpening filters are based on spatial
differentiation
Spatial Differentiation
Differentiation measures the rate of change of a
function
Let’s consider a simple 1 dimensional example
Spatial Differentiation
A B
1st Derivative
It’s just the difference between subsequent
values and measures the rate of change of the
function
f f (x +1) - f (x)
x
The formula for the 1st derivative of a function is as follo
ws:
• Requirement of first order derivative:
– Must be zero in flat segment
– Must be nonzero at the onset of a gray level step
or ramp
– Must be nonzero along ramps.
2nd Derivative
Simply takes into account the values both before
and after the current value
f (x +1) + f (x -1) - 2 f (x) 2 x
The formula for the 2nd derivative of a function is as fo
llo2wf
s:
• Requirement of second order derivative:
– Must be zero in flat reas
– Must be nonzero at the onset and at the end of a
gray level step or ramp
– Must be zero along ramps.
Using Second Derivatives For
Image
Enhancement The 2nd derivative is more useful for image enhancement than the 1st derivative
– Stronger response to fine detail
– Simpler implementation
The first sharpening filter we will look at is the Laplacian
– Isotropic(rotation invariant)
– One of the simplest sharpening filters
– We will look at a digital implementation
Variants On The Simple
Laplacian There are lots of slightly different versions of the
Laplacian that can be used: 0 1 0
1 -4 1
0 1 0
1 1 1
1 -8 1
1 1 1
-1 -1 -1
-1 9 -1
-1 -1 -1
Simple
Laplacian
Variant of
Laplacian
Sobel Operators
To filter an image it is filtered using both operators
the results of which are added together
-1 -2 -1
0 0 0
1 2 1
-1 0 1
-2 0 2
-1 0 1
The Two-Dimensional DFT and Its Inverse
(a)f(x,y) (b)F(u,y) (c)F(u,v)
The 2D DFT F(u,v) can be obtained by
1. taking the 1D DFT of every row of image f(x,y), F(u,y),
2. taking the 1D DFT of every column of F(u,y)
Basics of Filtering in the Frequency Domain
Some Basic Filters and Their Functions
Lowpass filter
Highpass filter
Ideal Lowpass Filters (ILPFs)
• The simplest lowpass filter is a filter that “cuts off” all high-
frequency components of the Fourier transform that are at a
distance greater than a specified distance D0 from the origin of
the transform.
• The transfer function of an ideal lowpass filter
where D(u,v) : the distance from point (u,v) to the center of
ther frequency rectangle
2
1 2 2 D(u, v) (u - M / 2) + (v - N / 2)
if D(u, v) D0
0 if D(u, v) D0
H (u, v) 1
Ideal Lowpass Filters (ILPFs)
Ideal Lowpass Filters (ILPFs)
Ideal Lowpass Filters
Butterworth Lowpass Filters (BLPFs) With order n
2n
0
1
1+ D(u, v) / D H (u, v)
Butterworth Lowpass Filters (BLPFs)
n=2
D0=5,15,30,80,and 230
Gaussian Lowpass Filters (FLPFs)
2 0
2 - D (u,v) / 2D H (u, v) e
Gaussian Lowpass Filters (FLPFs)
D0=5,15,30,80,and 230
Additional Examples of Lowpass Filtering
Sharpening Frequency Domain Filter
Hhp (u, v) Hlp (u, v)
Ideal highpass filter
Butterworth highpass filter
Gaussian highpass filter
if D(u, v) D0
1 if D(u, v) D0
H (u, v) 0
2n
0
1
1+ D / D(u, v) H (u, v)
2 0
2 -D (u,v) / 2D H (u, v) 1- e
Highpass Filters Spatial Representations
Ideal Highpass Filters
1 if D(u, v) D0
if D(u, v) D0 H (u, v)
0
Butterworth Highpass Filters
2n
0
1
1+ D / D(u, v) H (u, v)
Gaussian Highpass Filters
2 0
2 -D (u,v) / 2D H (u, v) 1- e
Image restoration
A model of image
degradation/restoration
process
Electronic noise/poor
illumination
Laser imaging Range imaging (sensor)
Laser imaging Quick transients (faulty switching)
• Periodic noise reduction by
– Spatial filters
– Frequency domain filters
Approach f(x,y)
Build
degradation model
Formulate
restoration algorithms
f(x,y)
Analyze using
algebraic techniques
Implement using
Fourier transforms
g = h*f + n
g = Hf + n
W -1 g = DW -1 f + W -1 n
f = H -1 g
F(u,v) = G(u,v)/H(u,v)
Unit 2 Questions
Q1)
Q2)
Q3)
Q4)
Q5)
Q6)
Q7)
Q8)
Q9)
Q10)
Q11)
Q12)
Q13)
Q14)
Q15)
Q16)
Q17)
Q18)
Q19)