digital image processing, image processing

24-09-2013©Kalyan Acharjya 1
Introduction to Digital Image Processing
Digital Image Processing in MATLAB
Two Live Applications of Digital Image Processing
75 Min
120 Min
30 Min
©KALYANACHARJYA

A Lecture on
Introduction to
DIGITAL IMAGE PROCESSING
24-09-2013©Kalyan Acharjya
2
Presented By
Kalyan Acharjya
Assistant Professor, Dept. of ECE
Jaipur National University

Sorry, shamelessly I opened the lock without prior permission taken
from the original owner. Some images used in this presentation contents
are copied from internet without permission.
Only Original Owner has full rights reserved for copied images.
This PPT is only for fair academic use.
Kalyan Acharjya

Objective of Two Hour Presentation
“To introduce the basic concept
of Digital Image Processing”

Contents
5
 What is image and Image
Processing?
 Image Understanding.
 Why Digital image Processing ?
 Digitization of Image.
 Histogram and Thresholding of
Image.
 Noise and its Extraction from
Image.
 Edge Detection.
 Image Enhancement.
 Image Compression.
 Data Hiding in Image.
 Color Image Processing.
 Applications

What is image ?
And Image Understanding.

What is image?
7
 An image is a 2-D light intensity function f( x, y).
 An image is considered as Matrix.
 A digital image f( x, y) is described both in
spatial co-ordinates and Brightness.
• The points in the image and element value of matrix
identifies gray level value at that point.
This element is called pels or Pixels.
• So f( x, y)=R( x, y) *I(x, y)
Where R Reflectivity of Surface (Pixel Point)
I Intensity of incident Light
y
x

Matrix or Digital Representation of Image
8
• An Image has infinite intensity value.
• Also infinite picture point -How its stored.
• Digitization of image.
 Spatial discretization by Sampling.
 Intensity discretization by Quantization.
I=

Matrix as an Image
9
 An Matrix is an image for DIP

Types of Digital Images
10
 Binary Image: Each Pixel is just
Black and white, i.e. 0 or 1
 <462x493 logical>
 Gray Scale Image: Each Pixel is
shade of Gray, its 0(black) to
white(255),i.e. each pixel~8 bits
 <462x493 unit8>
 Color Image or RGB Image, Each
pixel corresponds to 3 values.
 <462x493x3 unit8>

Image Data Type
11
 int8-8 bit integer, Range -128 to 127
 unit8-8 bit unsigned integer, Range 0 to 225
 int16-16 bit integer, Range -32768 to 32767
 uint16-16 bit unsigned integer, Range 0 to 65535
 double-Double precision real number, Machine Specific

Levels of Image Understanding
12
 Low Level-Involve primitive operations.
e.g. Image Preprocessing, noise reduction,
Enhancement etc.
Image input - Image Out
• Mid Level-
Image segmentation, identify particular objects.
Image input - Attributes extracted from those images
e.g. edges, contour, identify etc.
• High Level-Involving making sense of an ensemble of recognize objects, image
analysis and far end the functions normally associated with human vision.
Image
• Processing
Image
• Analysis
Image
• Measurements

Lets look back !
When the Digital Image Processing Started ?

History of Image Processing
14
 Its not young field, In 1920 submarine cables were used to transmit digitalized
newspaper pictures between London and New-Work-Use Telegraphic Printing.
 In 1921 –Improved in printing, use photographic
printing to enhance the quality and resolution.
 Actually DIP/ Computer Processing Technique
was used to improve the pictures of moon
transmitted by RANGER 7 at JET PROPULSION LAB.
it’s the real beginning…

Why Digital Image Processing ?

Why Digital Image Processing
16
 How we stored the image: Reduce the size for storage .
 How analog image world is relate to digital processing world.
 Compression-Remove redundancies.
 Transmission with minimum bandwidth.
 Lossy Compression=redundancy +some information, but still acceptable.
Original Image
Size-116 KB
Compressed Image
Size-12.9 KB, 11 %
Compressed Image
Size-1.95 KB, 1.6 %

Why Digital Image Processing
17
 Image Enhancement :To improve the
interpretability or perception of
information in image.
 Spatial Domain Method.
 Frequency Domain Method.
• Moving Object Tracking
• Human-Computer Interaction
• Computer Vision etc.
Lena Central Compressed
Spatial Domain Frequency Domain

How Digitization of Image ?

Lets, little detail : Digitization of Image
19
a b
a b
a b a b
Fig-A-Continuous Image Fig-B-Gray level Variation from a to b
Fig-C-Sampling and Quantization Fig-D-Digital line from a to b

Bit Planes of Grey Scale image
20
 Grey scale images can be transformed into a sequence of binary images by
breaking them into bit planes.

Spatial and Gray Level Resolution
21
• Gray level L=2k
• L is discrete level allowed to
each pixel.
• M and N are spatial
• Halve and Double
• The number of bits required to
store digital image b=MxNxk
• When M=N, b= kN2

Basics operations with image.

Arithmetic and Logical Operations in images
23
 Say Image as y=f(x)
 This include add or subtract or multiply or
divide each pixel value by constant factor,
which may be pixel value of another image.
 Y=f(x)+/-/*c
 Complement: For gray scale image is its
photographic negative.
 Logical Operations: AND,OR,NOT in binary
image.

Resize an Image
24
 Interpolation
 Extrapolation

Histogram of Image.

Histograms of Images
26
 Histograms of Gray level image represents the numbers of times each gray
level occurs in the image.
 Dark image-the gray levels would be clustered at the lower end
 In a Uniformly bright image, the gray levels would be clustered at the
upper end.
 In a well contrasted image, the gray levels would be well spread out over
much of the range.

Importance of Histograms Graph
27
 In Poorly Contrast image, enhance by spreading out of its histograms.
There are two ways-
 Histograms stretching (contrast Stretching).
 Histogram Equalization.
 Histograms stretching:
• Poorly contrasted image in the range [a, b]
• Stretch the gray levels in the center of the range out by applying a
piecewise linear function.
• This function has the effect of stretching the gray levels [a, b] to
[c, d], where a<c and d>b

Histograms stretching (Cont.)
28
 The linear function imadjust(I, [a, b],[c, d])
 if Pixel value is less than c are all converted to c and pixel values greater
than d are all converted to d.
a b 1
c
d
1
Gamma<1 Gamma>1
Y= (
𝑥−𝑎
𝑏−𝑎
)^Gamma (d-c)+c

Gamma Scaling
29
 Its important for graphics and games, its relates to pixel intensities of the
image.
One horn RHINO at Kaziranga National Park, Assam

Histograms Equalization
30
 Histogram equalization is a technique for adjusting image intensities to
enhance contrast
• Histogram equalisation algorithm: Let be the
intensities of the image, and let be its normalised histogram
function. The intensity transformation function for histogram equalisation is
 That is, we add the values of the normalised histogram function from 1
to k to find where the intensity will be mapped. Notice that the range
of the equalised image is the interval [0,1].
mkrk ,...,2,1, 
)( krp


k
j
kk rprT
1
)()(
kr

Histogram Equalization
31
Original Image and It’s Histogram
Histogram Equalized Image and It’s Histogram

Thresholding of an image
32
 Single Threshold:
A gray scale image is turned into a binary image by first choosing a
gray level T in the original image.
Pixel Value>T tends to white (1)
Pixel Value<=T tends to black (0)
• Double Threshold:
A pixel becomes white if T1<pixel value<T2.
A pixel becomes black if gray level is others.

Noise Extraction from Image.

Noise and Its Extraction
34
 Noise is any degradation in the image signal caused by external
disturbance.
 Salt and pepper noise: It is caused by sharp and sudden disturbances in
the image.
 Gaussian noise: It is caused by random fluctuations in the signal. It can be
idealized form of white noise.
 Speckle noise: it is modeled by random values multiplied by pixel values. In
radar applications.
 Shot noise: The dominant noise in the lighter parts of an image from
an image sensor.

Removal of noise by Filtering
35
 Linear filter
 Median Filter
 Specific case of order statistic filtering.
 Remove salt & pepper noise.
 Adaptive Filter
 Weiner filter use to remove Gaussian
noise.

Extract Noise from an image
36
Image with Gaussian Noise Image after Noise removal
‘wiener2’ Filter

Spectral Filtering
37
 Spectral filtering is most commonly used to either select or eliminate
information from an image based on the wavelength of the information.
 Spectral selectivity is a technique for creating images which uses
intentionally limited ranges of radiation in the ultraviolet, visible or
infrared portions of the spectrum

Example High Pass Filtering
38
OriginalImage
HighPassfilteringresult
Highfrequency
emphasisresult
Afterhistogram
equalisation

Edge Detection of Image ?

What is Image Edge
40
 Edges are those places in an image that
correspond to object boundaries.
 Edges are pixels where image brightness
changes abruptly.
 It is a vector variable (magnitude of the
gradient, direction of an edge) .

Steps of Edge Detection
41
 Filtering – Filter image to improve performance of the Edge Detector with
respect to noise
 Enhancement – Emphasize pixels having significant change in local intensity
 Detection – Identify edges - Thresholding
 Localization – Locate the edge accurately, estimate edge orientation
 Types of Edges
 Step Edge
 Ramp Edge
 Line Edge
 Roof Edge

Edge Detection
42
Hey its our TAJMAHAL…!
What you have done…?

Edge Detection
43
 Motivation: Detect changes in the pixel value as large gradient.
 I(m , n)={
1 𝑔 𝑚, 𝑛 > 𝑇ℎ
0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
 Image x(m , n) Edge Map I(m, n)
 Prewitt Operator.
 Sobel Operator.
 Canny Edge Detector.
 Kirsch Compass Masks.
 Roberts Operator
Gradient
Operator
Thresholding

Basics Relationship Between Pixels
44
 Neighbors of pixel
 Adjacency, Connectivity
 4 Adjacency.
 8 Adjacency.
 m Adjacency.
Image Operations
 Point
 Local
 Global
 Region and Boundaries.
 Distance between Pixel
 Image operation on pixel basis.

Image Enhancement.

Image Enhancement Technique
46
 Basic Gray level transformation
 Histogram Modification
 Average and Median Filtering
 Frequency domain operations.
 Homomorphic Filtering.
 Edge Enhancement.
Image
Enhancement
Technique
Better Image
Spatial or Frequency Domain

Spatial Domain
47
 Point of interest is f( x, y)
 Contrast stretching
 All these point operation, hence its point processing.
f(x, y)
y
x
r
s
r > Input gray level
s > Output Gray level
s=T(r)
s=T(r)
s
r
Threshold

Image Enhancement in Frequency Domain
48
• To filter an image in the frequency domain:
 Compute F( u, v) the DFT of the image
 Multiply F( u , v) by a filter function H( u, v)
 Compute the inverse DFT of the result

DFT of Image
49
• The DFT of a two dimensional image can be visualised by showing the
spectrum of the images component frequencies.
DFT
y
x
v
u

Basic Frequency Domain Filters
50
Low Pass Filter
High Pass Filter

Ex. of Image Enhancement in FD
51
• Different low pass Gaussian filters used to remove blemishes in a photograph.

Frequency Domain Laplacian Example
52
Original Image
Laplacian Filtered
Image
Laplacian Image
Scaled
Enhanced image

Conclusion of Filtering
53
 Fourier transform in Image Processing in the frequency domain
 Image smoothing
 Image sharpening
 Fast Fourier Transform
 Image restoration using the spatial and frequency based techniques.

Information Hiding by Image Processing
55
 Steganography
It is the process of hiding of a secret message within an ordinary image.
• Watermarking
It is the process of hiding of a secret message within an ordinary image, but
carrier image must be unchanged.
Encoder Decoder

JPEG
Image Compression.
Size-270 KB Size-22 KB
“Without Compression a CD store only 200 Pictures or 8 Seconds Movie”

Image Compression-Lossy or Lossless
57
 Image compression is the process of reducing the amount of data required
to represent an image.
 But its resolution or features should be unchanged for human perception.
 Relative Data Redundancy Rd of the first data set is Rd=1-1/CR
where CR-Compression Ratio=n1/n2 ,n1 and n2 denote the nos. of information
carrying units in two data sets that represent the same information.
• In Digital Image Compression , the basics data redundancies are
 Coding Redundancy
 Inter pixel Redundancy
 Psycho-visual Redundancy

Image Compression General Models
58
 Some image Compression Standard
 JPEG-Based on DCT
 JPEG 2000-Based on DWT
 GIF-Graphics Interchange Format etc.
Source
Encoder
Channel
Encoder
Channel
Decoder
Source
Decoder
Channel/
Store
F(x, y)
F’(x, y)

Data ≠ Information
59
 Data and information are not synonymous terms!
 Data is the means by which information is conveyed.
 Data compression aims to reduce the amount of data required to represent
a given quantity of information while preserving as much information as
possible.
 Image compression is an irreversible process.
 Some Transform used for Image Compression
 DCT-Discrete Cosine Transform
 DWT-Discrete wavelet Transform etc

Color Image Processing

Color Image Processing
61
• RGB : Color Monitor, Color Camera, Color Scanner
• CMY : Color Printer, Color Copier
• YIQ : Color TV-Y(Luminance), I(In phase), Q(Quadrature)
 HSI, HSV

Color Issue of an Image
62
 Red, Green and Blue Color cube
 Consider Each element=8 bit
 R,G,B ~0 to 255
 Grey scale f(x , y , L)
 256 Grey shades
 Color Scale f(x , y, r , g , b)-24 bit
 255x255x255=16777216 colors

What a color image contains
63

RGB Components of an Image
64

CMY and CMYK Color Model
65
 Cyan(C), Magenta(M) and Yellow(Y) are the secondary colors of light.
• Or CMY are Primary colors of pigments.
 RGB to CMY
 Black=Cyan + Magenta + Yellow
 Printing Industry used to four color Printing.
 Cyan, Magenta, Yellow plus Black.

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B
G
R
Y
M
C
1
1
1

Where you start ?
Digital Image Processing !

Popular Image Processing Software Tools
67
 CVIP tools
(Computer Vision and Image Processing tools)
 Intel Open Computer Vision Library
 Microsoft Vision SDL Library
 MATLAB
 KHOROS

Applications of Digital Image Processing
69
 Identification.
 Robot vision.
 Steganography.
 Image Enhancement.
 Image Analysis in Medical.
 Morphological Image Analysis.
 Space Image Analysis.
 IC Industry……….etc.

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Email-kalyan.acharjya@gmail.com

digital image processing, image processing

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