Introduction to digital 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

© K

ALYA

N A

CH

AR

JYA

Introduction to image processing1.pptx

image processing in matlab .pptx

Live Applications of DIP.pptx

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

Origina

l Image

High P

ass

filte

ring

resu

lt

High f

requ

enc

y

emphasis

resu

lt

Aft

er

histo

gram

equ

alisa

tion


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.

© K

ALYA

N A

CH

AR

JYA

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.

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?

© K

ALYA

N A

CH

AR

JYA

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.

Agenda.pptx




https://twitter.com/Kalyan_online [email protected]

https://twitter.com/Kalyan_online

https://twitter.com/Kalyan_online

mailto:[email protected]





Introduction to digital image processing

Engineering

image input image

digitization of image

image enhancement

image compression

rgb image

image preprocessing

thresholding of image

color image processing