Lecture 1 Introduction to Digital Image Processing

Lecture 1 

Introduction to Digital 

Image Processing 

Assisst.Prof.Dr. Umut ARIÖZ 

Fall 2014

Introduction to the course 

Lecture Hours: 

• Thursday 13:30 - 16:20 

Textbook: 

• Digital Image Processing, 2. Edition, R.C. 

Gonzalez, R.E. Woods, Prentice Hall 2002. 

Assesstment Methods: 

• Participation 5% 

• Project 20% 

• Midterm Exam 35% 

• Final Exam 40% 

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Contact Info 

• Room 116 

• Phone: + 90 312 582 3116 

• E-mail: umut.arioz@gazi.edu.tr 

• Web-site: http://ceng.gazi.edu.tr/~uarioz/bm471_2015.html 

Syllabus 

All lecture notes 

Project details 

announcements 

2014 Fall

Projects 

• Web-based application 

• http://ceng.gazi.edu.tr/dsp/default.aspx similar content 

• Lecture assistant will follow 

• 4-5 persons per group 

2014 Fall

Syllabus 

1. Week 

Introduction to Digital Image Processing 

• Origins of DIP 

• Example Applications 

• Fundamental Steps in DIP 

• Components of DIP 

2. Week 

Digital Image Fundamentals 

• Visual Perception 

• Light and EM Spectrum 

• Sensing and Acquisition 

3. Week 

4. Week 

5. Week 

Digital Image Fundamentals 

• Sampling and Quantization 

• Relationships between pixels 

Image Enhancement in The Spatial Domain 

• Gray Level Transformations 

• Histogram Processing 

Image Enhancement in The Spatial Domain 

• Enhancement using logic operations 

• Spatial filtering 

2014 Fall

Syllabus 

6. Week 

7. Week 

Image Enhancement in The Frequency Domain 

• FT and Frequency Domain 

• Frequency Domain Filters 

Image Enhancement in The Frequency Domain 

• Homomorphing Filtering 

8. Week Midterm Exam 

9. Week 

10. Week 

Image Restoration 

• Noise models 

• Restoration in the presence of noise 

• Degradations 

• Inverse Filtering 

Color Image Processing 

• Color models 

• Color transformations 

• Color segmentation 

• Noise in color images 

2014 Fall 

11. Week 

Image Compression Techniques 

• Compression models 

• Error-free compression 

• Lossy compression

Syllabus 

12. Week 

13. Week 

Morphological Image Processing 

• Dilation and erosion 

• Opening and closing 

• Some algorithms 

• Gray scale processing 

Image Segmentation 

• Detection of discontinuities 

• Boundary detection 

• Thresholding 

• Region-based segmentation 

• Morphological watersheds 

14. Week Project Presentations (4 goups) 

15. Week Project Presentations (4 goups) 

16. Week Final Exam 

2014 Fall

This lecture will cover 

What is a digital image? 

What is digital image processing? 

History of digital image processing 

State of the art examples of digital image processing 

Key stages in digital image processing

2014 Fall

What is Digital Image Processing? 

•Digital image processing focuses on two major tasks 

Improvement of pictorial information for human interpretation 

Processing of image data for storage, transmission and 

representation for autonomous machine perception


Digital Image 

— a two-dimensional function x and y are spatial coordinates 

The amplitude of f is called intensity or gray level at the point (x, y) 

Pixel 

— the elements of a digital image 

f ( x, y) 

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•There are three levels from image processing to computer 

vision. 

Low Level Process 

Input: Image 

Output: Image 

Examples: Noise 

removal, image 

sharpening 

Mid Level Process 

Input: Image 

Output: Attributes 

Examples: Object 

recognition, 

segmentation 

High Level Process 

Input: Attributes 

Output: Understanding 

Examples: Scene 

understanding, 

autonomous navigation 

In this course we will 

stop here

Image Processing 

• manipulation of multidimensional signals 

image (photo) 

video 

f 

f 

( x, 

y) 

( x, 

y, 

t) 

CT, MRI 

f ( x, 

y, 

z, 

t)

FROM ANALOG TO DIGITAL 

Imaging 

systems 

Sample and 

quantize 

Digital 

storage 

(disk) 

Digital 

computer 

Display 

output 

object 

observe 

digitize 

store 

process

What is a Digital Image? 

•A digital image is a representation of a two-dimensional 

image as a finite set of digital values, called picture 

elements or pixels

What is a Digital Image? (cont…) 

•Pixel values typically represent gray levels, colours, heights, opacities 

etc 

•Remember digitization implies that a digital image is an 

approximation of a real scene 

1 pixel

What is a Digital Image? (cont…) 

•Common image formats include: 

1 sample per point (B&W or Grayscale) 

3 samples per point (Red, Green, and Blue) 

4 samples per point (Red, Green, Blue, and Opacity) 

5 samples per point (Red, Green, Blue, Opacity and Depth) 

Binary ( 0, 1) Grayscale (0, 255) Color (3*(0, 255))

Digital Image 

a grid of squares, each of which 

contains a single color 

Color images have 3 values per 

pixel; monochrome images have 

1 value per pixel. 

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Color Images 

l 

l 

l 

Are constructed from three 

intensity maps. 

Each intensity map is projected 

through a color 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 color image. 

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25 September 2014

Color Sensing / Color Perception 

luminance hue saturation 

The eye has 3 types of photoreceptors: 

sensitive to red, green, or blue light. 

The brain transforms RGB into separate 

brightness and color channels (e.g., LHS). 

brain 

photo receptors 

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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

History of Digital Image Processing 

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Sent by submarine cable 

between London and New 

York, the transportation time 

was reduced to less than 

three hours from more than a 

week

History of DIP (cont…) 

•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


•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


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•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


•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

Sources for Images 

One of the simplest ways to develop a basic understanding of the extent 

of image processing applications is categorization according to sources. 

• Electromagnetic (EM) energy spectrum 

• Synthetic images produced by computer 

• Acoustic 

• Ultrasonic 

• Electronic 

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Electromagnetic (EM) energy spectrum 

• Images based on radiation from the EM spectrum are the most familiar to us. 

• If bands are grouped according to energy per photon, we obtain the spectrum 

• Em spectrum are not distinct but rather transition smoothly one to other. 

Major uses 

Gamma-ray imaging (highest energy): nuclear medicine and astronomical observations 

X-rays: medical diagnostics, industry, and astronomy, etc. 

Ultraviolet: industrial inspection, microscopy, lasers, biological imaging, and astronomical observations 

Visible and infrared bands: light microscopy, astronomy, remote sensing, industry, and law enforcement 

Microwave band: radar 

Radio band (lowest energy) : medicine (such as MRI) and astronomy 

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Examples: Gama-Ray Imaging 

• The approach is to inject a patient with a radioactive isotope that emits 

gamma rays as it decays. 

• Images are produced from the emissions collected by gamma ray detectors. 

• Positron Emission Tomography (PET) 

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Examples: X-Ray Imaging 

• Oldest sources of EM radiation used for 

imaging. 

• Not ony for medical diagnositics, also used for 

astronomy, electronics and security. 

• Two ways for digital imaging in radiography: 

digitizing x-ray films 

Capturing by a light sensitive digitizing system from 

phosphor screen that converts x-rays to light. 

• Image Guided Surgery 

Angiogram: catheter is threaded into the 

blood vessel and guided to the area to be 

studied. 

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Weeks 1 & 2

Examples: Ultraviolet Imaging 

• Usage areas: 

 

 

 

 

 

Industrial inspection 

Microscopy 

Lasers 

Biological imaging 

Astronomical observations 

• Fluorescenes microscopy is an excellent 

method for studying materials for 

determining the natural form. 

Normal corn 

Infected corn 

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Examples: Light Microscopy Imaging 

• Applications in the visual band 

 

 

 

 

 

Light microscope 

Astronomy 

Remote sensing 

Industry 

Law enforcement 

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Weeks 1 & 2

Examples: Visual and Infrared Imaging 

2014 Fall


Multispectral Imaging: one image for different spectral infrared bands 

 

 

Different views of buildings, roads, rivers 

Different purposes for each band (population growth, pollution, environment) 

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Examples: Infrared Satellite Imaging 

• Nighttime lights of the world 

• Provides global inventory of human settlements 

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• To inspect for missing parts 

 

 

Missing part: Black square on top 

Missing pills 

• For controlling 

 

 

Level of bottle 

Air pockets in plastic 

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Weeks 1 & 2


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

Example: Microwave Band (Radar Image) 

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Examples: Radio Band (MRI) 

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Examples: Radio Band (MRI)

Examples: Ultrasound Imaging 

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Some Image Processing Applications 

2014 Fall

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: HCI 

•Try to make human computer 

interfaces more natural 

Face recognition 

Gesture recognition

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Key Stages in Digital Image Processing 


Morphological 

Processing 

Image Enhancement 

Mid-level 

Segmentation 

Low-level 

Image Acquisition 

Object Recognition 

Problem Domain 

Representation & 

Description 

Colour Image 


Image Compression



Morphological 



Segmentation 





Description 




Key Stages in Digital Image Processing: 


• First step of DIP 

• Shows the origin of the images 

Topics: 

• Basic digital image concepts 

• Preprocessing stages 

• Visual perception 

• Sampling 

• Quantization 

• Pixel operations 


Problem Domain



Morphological 



Segmentation 





Description 






•Simplest and most attractive area of DIP 

•Idea is to bring out the details that is obscured or hide 

and to highlight certain features of interest in an image 


•Final words should be «it looks better» 

•So very subjective area, it can change according to 

everyone 


Problem Domain



•Enhancement in the spatial domain 

•Point processing 

Log transformation 

Power law transformation 


•Histograms 

What is an image histogram? 

Histogram equalisation 

•Spatial filtering process 



•Smoothing filters



•Frequency Domain Filtering 

The Fourier transform 

• Importance of the inverse Fourier transform 


filtering in the frequency domain 

Low pass filters - High pass filters 

• Ideal low pass filter 

• Butterworth low pass filter 

• Gaussian low pass filter 


Problem Domain



Original Image 

Fourier Transform 

Amplitude Phase 

•This is the example of FT 

• An image can be represented as an amplitude and phase in 

frequency domain.



Original Image 

High Pass Filtering



2014 Fall



•One of the most common uses of DIP techniques: 

improve quality, remove noise etc



Morphological 



Segmentation 





Description 






•Deals with improving the appearence of image 


• difference from enhancement is being objective 

•Based on mathematical or probabilistic models 

•Enhancement is based on human preferences 



Problem Domain

Distortion due to Camera Misfocus 

Original image 

Distorted image

Distortion due to Camera Misfocus 

Camera lens

Distortion due to motion 

Camera lens

Distortion due to Random Noise 

Original image 

Distorted image



•noise removal 


•noise models 

Common noise models 

• Gaussian 

• Rayleigh 

• Erlang 

•Filtering to remove noise 

Simple mean filter 

Other mean filters 

• Exponential 

• Uniform 

• Impulse (salt & pepper) 



Problem Domain



•Order statistics filters 

Median filter 

Max and min filter 

Midpoint filter 

Alpha trimmed mean filter 

•Removing noise in the frequency domain 

Particularly good for removing periodic noise 

Band reject filters 

• Ideal band reject filter 

• Butterworth band reject filter 

• Gaussian band reject filter 




Problem Domain



Distorted Image 

Restored Image


Image Restoration



Morphological 



Segmentation 





Description 




Morphological Image Processing 

•Used for extracting image components that are 

useful in the representation and description of 

shape 

Morphological 


•Basic morphological concepts and operations 

Hitting, fitting and missing 

Erosion and dilation 

Opening and closing 



•Morphological algorithms 

Boundary extraction 

Region filling 


• Extensions to gray-scale images 

Problem Domain



Morphological 



Segmentation 





Description 





Segmentation 

Segmentation 

•Generally provides an image into its 

constituent parts or objects 

Morphological 


•One of the most difficult tasks in DIP 


•Simple algorithms get failure at most times 


•More accurate segmentation means more 


accurate recognition 

Problem Domain


Segmentation 

Main topics: 

Segmentation 

•The segmentation problem 

Morphological 


•Importance of good thresholding 

•Problems that can arise with thresholding 


•The basic global thresholding algorithm 

•Point- edge detection 


•Region-based segmentation 


Problem Domain


Segmentation 

•Take slice from MRI scan of dog 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



Morphological 



Segmentation 





Description 






Segmentation 

• Basically, it assigns a label 

(vehicle, human, …) to an 

object on its descriptors 

Morphological 



Topics: 

• Pattern classes 

• Structural methods 




Problem Domain



Morphological 



Segmentation 





Description 





Representation & Description 

• Always follow the output of 

segmentation stage 

• Description is also called 

feature selection 

Morphological 


Segmentation 


• Deals with extracting attributes 

Topics: 

• Chain codes 

• Skeletons 

• Boundary descriptors 

• Regional descriptors 

• texture 






Description



Morphological 



Segmentation 





Description 





Image Compression 

Segmentation 

• For reducing the storage required 

to save an image or bandwidth 

required to transmit it. 

• Most popular – jpeg 

Topics: 

• Coding redundancy 

• Image compression models 

• Error-free compression 

• Lossy compression 

• Image compression standards 

Morphological 








Description 




Morphological 



Segmentation 





Description 





Colour Image Processing 

Segmentation 

Topics: 

• Color fundamentals 

• Color models 

• Color transformations 

• Smoothing and sharpening 

• Color segmentation 

• Noise in color images 

Morphological 







Description 



Problem Domain

Color Photo Enhancement 

2014 Fall

Next lecture will cover 

Elements of Visual Perception 

Light and EM 

Image Sensing and Acquisiton 

Sampling and Quantization 

Relationships between pixels 

2014 Fall

Lecture 1 Introduction to Digital Image Processing

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