Digital image processing using matlab

Digital Image Processing using Matlab

Introduction

Eng.Amr Ez El_Din Rashed

Assistant Lecturer

Taif university.

Tel:0554404723 e-mail:[email protected]

2

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38 Introduction

“One picture is worth more than ten thousand words”

Anonymous

3

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38 References

“Digital Image Processing using Matlab”, Rafael

C. Gonzalez & Richard E. Woods,

Addison-Wesley, 2004

or

“Computer Vision and image processing

processing A practical approach using cvip tools

Scott E umbaugh

Prentice hall 1998

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38

An Introduction to Digital Image Processing with

Matlab,2004

School of Computer Science And Mathematics

Victoria University of Technology

Biosignal and Biomedical Image Processing

Matlab-Based Application,

JOHN L. SEMMLOW

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38 Course

• This course will cover:

Introduction to Image Processing And Computer

Vision.

Image Fundamentals.

Intensity Transformation and Spatial Filtering

Frequency Domain Processing

Image Restoration

Wavelets

Morphological Image Processing

Image Segmentation

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38 Contents

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

7

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38 Images

an image is a single picture which represents

something. It may be a picture of a person, of people

or animals, or of an outdoor scene, or a

microphotograph of an electronic component, or the

result of medical imaging.

Even if the picture is not immediately recognizable, it

will not be just a random blur.

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38 Computer imaging

• It’s defined as the acquisition and processing

of visual information by computer.

• The ultimate receiver of information is:

– Computer

– Human visual system

• So we have two categories:-

– Computer vision

– Image processing

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Computer vision and image

processing

• In computer vision:

–The processed output images

are for use by computer.

• In Image processing:

–The output images are for

human consumption

10

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38 Computer vision

• One of the computer vision fields is image

analysis.

• It involves the examination of image data

to facilitate solving a vision problem.

• Image analysis has 2 topics:

– Feature extraction: acquiring higher level

image information

– Pattern classification taking these higher level

of information and identifying objects within

the image

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38 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

12

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38 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

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38 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)

For most of this course we will focus on grey-scale

images

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38 What is Digital Image Processing?

Digital image processing involves changing

the nature of an image in order to either

– Improve it’s pictorial information for human

interpretation(image ehancement)

– Processing of image data for storage,

transmission and representation for

autonomous machine perception(ادراك)

Some argument about where image

processing ends and fields such as image

analysis and computer vision start

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38 DIP(cont…)

• It is necessary to realize that these two aspects

represent two separate but equally important

aspects of image processing. A procedure

which satisfies condition.

• (1)a procedure which makes an image look

better may be the very worst procedure for

satisfying condition

• (2). Humans like their images to be sharp, clear

and detailed; machines prefer their images to

be simple and uncluttered.

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38 Example of (1)

Enhancing the edges of an image to make it appear sharper

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38 Example of (1)

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38 Example of (1)

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38 Example of (2)

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38 Example of (2)

21

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38 What is DIP? (cont…)

The continuum ( متسلسل -متصل )from image

processing to computer vision can be

broken up into low-, mid- and high-level

processes 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

22

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38 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 (5 tones)

– 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

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38 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

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38 History of DIP (cont…)

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

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38 History of DIP (cont…)

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

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38 History of DIP (cont…)

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

27

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38 Examples: Image Enhancement

One of the most common uses of DIP

techniques: improve quality, remove noise

etc

28

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38 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

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38 Examples: Artistic Effects

Artistic effects are

used to make

images more

visually appealing,

to add special

effects and to make

composite images

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38 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

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38 Examples: GIS

Geographic Information Systems

– Digital image processing techniques are used

extensively to manipulate satellite imagery

– Terrainتضاريس classification

– Meteorology األرصاد الجوية

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38 Examples: GIS (cont…)

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

33

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38 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

Can we trust them?

34

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38 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

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38 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

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38 Examples: HCI

Try to make human computer

interfaces more natural

– Face recognition

– Gesture ايماءةrecognition

Does anyone remember the

user interface from “Minority

Report”?

These tasks can be

extremely difficult

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38 Key Stages in Digital Image Processing

Image

Acquisition

Image

Restoration

Morphological

Processing

Segmentation

Representation

& Description

Image

Enhancement

Object

Recognition

Problem Domain

Colour Image

Processing

Image

Compression

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Key Stages in Digital Image Processing:

Image Aquisition

Image

Acquisition

Image

Restoration

Morphological

Processing

Segmentation

Representation

& Description

Image

Enhancement

Object

Recognition

Problem Domain

Colour Image

Processing

Image

Compression

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Image Enhancement:

taking an image and improving it visually

Image

Acquisition

Image

Restoration

Morphological

Processing

Segmentation

Representation

& Description

Image

Enhancement

Object

Recognition

Problem Domain

Colour Image

Processing

Image

Compression

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Image Restoration : taking an image with some known or estimated degradation and restoring

it to its original appearing

Image

Acquisition

Image

Restoration

Morphological

Processing

Segmentation

Representation

& Description

Image

Enhancement

Object

Recognition

Problem Domain

Colour Image

Processing

Image

Compression

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Key Stages in Digital Image Processing:

Morphological Processing

Image

Acquisition

Image

Restoration

Morphological

Processing

Segmentation

Representation

& Description

Image

Enhancement

Object

Recognition

Problem Domain

Colour Image

Processing

Image

Compression

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Key Stages in Digital Image Processing:

Segmentation

Image

Acquisition

Image

Restoration

Morphological

Processing

Segmentation

Representation

& Description

Image

Enhancement

Object

Recognition

Problem Domain

Colour Image

Processing

Image

Compression

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Key Stages in Digital Image Processing:

Object Recognition

Image

Acquisition

Image

Restoration

Morphological

Processing

Segmentation

Representation

& Description

Image

Enhancement

Object

Recognition

Problem Domain

Colour Image

Processing

Image

Compression

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Key Stages in Digital Image Processing:

Representation & Description

Image

Acquisition

Image

Restoration

Morphological

Processing

Segmentation

Representation

& Description

Image

Enhancement

Object

Recognition

Problem Domain

Colour Image

Processing

Image

Compression

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Image compression: reducing the massive amount of data needed to represent an image

Image

Acquisition

Image

Restoration

Morphological

Processing

Segmentation

Representation

& Description

Image

Enhancement

Object

Recognition

Problem Domain

Colour Image

Processing

Image

Compression

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Key Stages in Digital Image Processing:

Colour Image Processing

Image

Acquisition

Image

Restoration

Morphological

Processing

Segmentation

Representation

& Description

Image

Enhancement

Object

Recognition

Problem Domain

Colour Image

Processing

Image

Compression

47

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38 Sampling

• Sampling refers to the process of digitizing a continuous function. For

example, suppose we take the function

• and sample it at ten evenly spaced values of x only. The resulting sample

points are shown in figure 1.6. This shows an example of under sampling,

where the number of points is not sufficient to reconstruct the function.

Suppose we sample the function at 100 points, as shown in figure 1.7. We

can clearly now reconstruct the function; all its properties can be determined

from this sampling. In order to ensure that we have enough sample points,

we require that the sampling period is not greater than one-half the finest

detail in our function. This is known as the Nyquist criterion, and can be

formulated more precisely in terms of “frequencies”. The Nyquist criterion

can be stated as the sampling theorem, which says, in effect, that a

continuous function can be reconstructed from its samples provided

that the sampling frequency is at least twice the maximum frequency in

the function. A formal account of this theorem is provided by

Castleman

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38 Image Acquisition and Sampling

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38 Effect of Sampling

Sampling an image requires that we consider the Nyquist criterion, when we consider an

image as a continuous function of two variables, and we wish to sample it to produce a

digital image. An example is shown ,where an image is shown, and then with an under

sampled version. The jagged edges in the under sampled image are examples of

aliasing. The sampling rate will of course affect the final resolution of the image. In order

to obtain a sampled (digital) image, we may start with a continuous representation of a

scene. To view the scene, we record the energy reflected from it; we may use visible

light, or some other energy source.

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38 Data Classes

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38 Types of digital images

The toolbox supports four types of images:

1-intensity images.

2-binary images.

3-Indexed images.

4-RGB images.

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38 Intensity images(grayscale images)

• An intensity image is a data matrix whose values have

been scaled to represent intensities.

• Class uint8 :range [0,255]

• Class uint16:range[0,65535]

• Double: floating point[0,1]

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38 Binary image

Binary. Each pixel is just black or white. Since there are only two possible

values for each pixel,

we only need one bit per pixel. Such images can therefore be very efficient in

terms of storage. Images for which a binary representation may be suitable

include text (printed or handwriting), fingerprints, or architectural plans.

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38 True color, or RGB image

Here each pixel has a particular color; that color being described by the amount of red,

green and blue in it. If each of these components has a range 0-255 this gives a total

of different possible colors in the image 255^3=16,777,216. This is enough colors for

any image. Since the total number of bits required for each pixel is 24, such images

are also called 24 bit color images. Such an image may be considered as consisting of

a “stack” of three matrices; representing the red, green and blue values for each pixel.

This means that for every pixel there correspond three values.

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38 Indexed image

Most color images only have a small subset of the more than sixteen million

possible colors. For convenience of storage and file handling, the image has

an associated color map, or color palette, which is simply a list of all the

colors used in that image. Each pixel has a value which does not give its

color (as for an RGB image), but an index to the color in the map.

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38 Converting between Image Classes andTypes

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• Image files tend to be large. We shall investigate the amount of information

used in different image type of varying sizes. For example, suppose we

consider a binary image. The number of bits used in this image (assuming

no compression, and neglecting, for the sake of discussion, any Header

information) is

512×512×1=262,144

=32768 bytes

• If we now turn our attention to color images, each pixel is associated with 3

bytes of color Information. A 512×512×3 image thus requires

512×512×3=786,432Byte

• =786.43Kb

• =0.786 Mb.

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38 Import pixel information

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38 ind2gray

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• Much of image processing is concerned with making an image

appear better to human beings. We should therefore be aware

of the limitations of the human visual system. Image perception

consists of two basic steps:

• 1. Observed intensities vary as to the background. A single

block of grey will appear darker if placed on a white background

than if it were placed on a black background.

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38 Cont.

• 2.We may observe non-existent intensities as bars in

continuously varying grey levels. This image varies

continuously from light to dark as we travel from left to

right. However, it is impossible for our eyes not to see a

few horizontal edges in this image.

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3. Our visual system tends to undershoot or overshoot

around the boundary of regions of different intensities.

For example, suppose we had a light grey blob on a

dark grey background. As our eye travels from the

dark background to the light region, the boundary of

the region appears lighter than the rest of it.

Conversely, going in the other direction, the boundary

of the background appears darker than the rest of it.

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38 image

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• will certainly display the cameraman, but possibly in an odd

mixture of colors, and with some stretching. The strange

colors come from the fact that the image command uses the

current color map to assign colours to the matrix elements.

The default color map is called jet, and consists of 64 very

bright colours, which is inappropriate for the display of a

grayscale image.

• To display the image properly, we need to add several extra

commands to the image line.

• 1.truesize.

• 2.axis off

• 3. colormap(gray(247)), which adjusts the image colour map

to use shades of grey only. We can find the number of grey

levels used by the cameraman image with

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image(x),truesize,axis off,

colormap(gray(512))

will produce a dark image. This happens because only

the first 247 elements of the color map will be used by the

image for display, and these will all be in the first half of

the color map; thus all dark greys.

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image(x),truesize,axis off,

colormap(gray(128))

• will produce a very light image, because any

pixel with grey level higher than 128 will simply

pick that highest grey value (which is white) from

the color map.

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38 imshow

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To display the matrix cd, we need to scale it to the range

[0,1]. This is easily done simply by dividing all values by 255:

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38 im2double

We can convert the original image to double more properly

using the function im2double. This applies correct scaling so

that the output values are between 0 and 1.

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• Grayscale images can be transformed into a sequence of

binary images by breaking them up into their bit-planes. If we

consider the grey value of each pixel of an 8-bit image as an 8-

bit binary word,

• then the 0th bit plane consists of the last bit of each grey

value.Since this 0th bit plane has the least effect in terms of the

magnitude of the value, it is called the least significant bit, and

the plane consisting of those bits the least significant bit plane.

• Similarly the 7th bit plane consists of the first bit in each value.

This bit has the greatest effect in terms of the magnitude of the

value, so it is called the most significant bit, and the plane

consisting of those bits the most significant bit plane.

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38 Spatial Resolution

• Spatial resolution is the density of pixels over

the image: the greater the spatial resolution, the

more pixels are used to display the image. We

can experiment with spatial resolution with

Matlab's

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38 Point Processing

• Any image processing operation transforms the grey

values of the pixels. However, image processing

operations may be divided into three classes based on

the information required to perform the transformation.

From the most complex to the simplest, they are:

• 1.Transforms.

• 2. Neighborhood processing.

• 3.Point operations : They are especially useful in

image pre-processing, where an image is required to

be modified before the main job is attempted.

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38 Arithmetic operations

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imadd output

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y=x/2 y=x*2 y=x/2+128

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38 Complements

The complement of a grayscale image is its

photographic negative. If an image matrix m is of type

double and so its grey values are in the range [0,1].

If the image is binary, we can use

• If the image is of type uint8, the best approach is the

imcomplement function he complement function y=255-x

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Given a grayscale image, its histogram consists of

the histogram of its grey levels; that is, a graph

indicating the number of times each grey level

occurs in the image.

1-In a dark image, the grey levels (and hence the

histogram) would be clustered at the lower end.

2-In a uniformly bright image, the grey levels would

be clustered at the upper end.

3-In a well contrasted image, the grey levels would

be well spread out over much of the range.

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Histogram Stretching (Contrast stretching)

• Given a poorly contrasted image, we would like

to enhance its contrast, by spreading out its

histogram. There are two ways of doing this.

1.Histogram stretching (contrast stretching).

2.Histogram equalization

2Histogram Stretching (Contrast stretching)

Histogram Stretching (Contrast stretching)

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Suppose we have an image with the histogram shown , associated

with a table of the numbers ni of gray values

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We can stretch the grey levels in the centre of the range out by

applying the piecewise linear function shown at the right in figure.

This function has the effect of stretching the grey levels 5-9 to

gray levels 2-14 according to the equation

Where I is the original gray level and j its result after the

transformation .This yields

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which indicates an image with greater contrast than the original.

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38 Use of imadjust

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A piecewise linear stretching function

The heart of our function will be the lines

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38 2.Histogram equalization

• The trouble with any of the above methods of

histogram stretching is that they require user input.

Sometimes a better approach is provided by

histogram equalization, which is an entirely automatic

procedure. The idea is to change the histogram to one

which is uniform; that is that every bar on the

histogram is of the same height, or in other words that

each grey level in the image occurs with the same

frequency. In practice this is generally not possible,

although as we shall see the result of histogram

equalization provides very good results.

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Suppose a 4-bit gray scale image has the histogram shown in figure associated

with a table of the numbers

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We now have the following transformation of grey values, obtained by reading o

the first and last columns in the above table:

This is far more spread out than the original histogram, and so the resulting image

should exhibit greater contrast.

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We give one more example, that of a very dark image. We can obtain a dark image by

taking an image and using imdivide.

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38 Lookup tables

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38 Linear filter

A diagram illustrating the process for performing spatial filtering is

given in figure.

Spatial filtering thus requires three steps:

1. position the mask over the current pixel.

2. form all products of filter elements with the corresponding

elements of the neighborhood.

3. add up all the products. This must be repeated for every pixel in

the image.

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125

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If we continue in this manner, the following output is obtained:

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mean filter output

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original image

130

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38 Another solution

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38 Another solution

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38 Cont.

• filter2(filter,image,'valid') applies the mask only to

inside pixels. The result will always be smaller than

the original:

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• The result of 'same' above may also be obtained by

padding with zeros and using 'valid':

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filter2(filter,image,'full') returns a result larger than the original;

it does this by padding with zero, and applying the filter at all

places on and around the image where the mask intersects

the image matrix.

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• The shape parameter, being optional, can be omitted; in which

case the default value is 'same'. There is no single best

approach; the method must be dictated by the problem at hand;

by the filter being used, and by the result required.

We can create our filters by hand, or by using the fspecial

function; this has many options which makes for easy creation

of many different filters. We shall use the average option, which

produces averaging filters of given size; thus

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• The resulting image after these filters may appear to

be much worse than the original. However, applying

a blurring filter to reduce detail in an image may the

perfect operation for autonomous machine

recognition, or if we are only concentrating on the

gross aspects of the image: numbers of objects;

amount of dark and light areas. In such cases, too

much detail may obscure the outcome.

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38 Separable filters

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38 Frequencies; low and high pass filters

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laplacian log

In each case, the sum of all the filter elements is zero.

High pass filtering

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151

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38 Rank order filter

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geometric mean filter, alpha-trimmed

mean filter

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38 The Fourier Transform

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38 2D –DFT,Properties

170

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38 Cont.

171

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38 Convolution

172

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38 DC Coefficient

173

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38 shifting

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38

175

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38 trouble

176

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38 Fourier transforms in Matlab

177

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38 Example

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38 Example 2.

Now we'll take a matrix consisting of a single corrugation:

179

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38 Example 3

• We will take here a single step edge:

180

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38 Fourier transforms of images(spectrum)

181

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38 FFTshow function

182

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38 Example 1

183

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38 Example 2(box)

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38 Example 3(box rotated 45)

185

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38 Example 4.(circle)

186

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38 Notes

• Note the ringing in the Fourier transform. This is an

artifact associated with the sharp cut off of the circle.

As we have seen from both the edge and box images

in the previous examples, an edge appears in the

transform as a line of values at right angles to the

edge. We may consider the values on the line as

being the coefficients of the appropriate corrugation

functions which sum to the edge. With the circle, we

have lines of values radiating out from the circle;

these values appear as circles in the transform.

187

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38 Example 5

188

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38 Filtering in the frequency domain

• Ideal filtering(low pass filter)

189

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38 Cont.

190

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38 Cont.

191

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38 Notes

• Note that even though cfli is supposedly a matrix of real

numbers, we are still using fftshow to display it. This is because

the fft2 and fft2 functions, being numeric, will not produce

mathematically perfect results, but rather very close numeric

approximations . So using fftshow with the 'abs' option rounds

out any errors obtained during the transform and its inverse.

Note the ringing about the edges in this image. This is a direct

result of the sharp cutoff of the circle. The ringing as shown in

figure is transferred to the image.

• We would expect that the smaller the circle, the more blurred

the image, and the larger the circle; the less blurred.

192

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38 Cont.

193

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38 High pass filtering

194

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38 Example

195

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38 Image Restoration

• Image restoration concerns the removal or reduction of

degradations which have occurred during the acquisition

of the image. Such degradations may include noise,

which are errors in the pixel values, or optical effects

such as out of focus blurring, or blurring due to camera

motion. We shall see that some restoration techniques

can be performed very successfully using neighborhood

operations, while others require the use of frequency

domain processes. Image restoration remains one of the

most important areas of image processing, but in this

chapter the emphasis will be on the techniques for

dealing with restoration, rather than with the

degradations themselves, or the properties of electronic

equipment which give rise to image degradation.

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38 Noise

• We may define noise to be any degradation in the image signal,

caused by external disturbance. If an image is being sent

electronically from one place to another, via satellite or wireless

transmission, or through networked cable, we may expect

errors to occur in the image signal. These errors will appear on

the image output in different ways depending on the type of

disturbance in the signal.

• Usually we know what type of errors to expect, and hence the

type of noise on the image; hence we can choose the most

appropriate method for reducing the effects. Cleaning an image

corrupted by noise is thus an important area of image

restoration.

197

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38 Salt and pepper noise

• Also called impulse noise, shot noise, or

binary noise. This degradation can be

caused by sharp, sudden disturbances in

the image signal; its appearance is

randomly scattered white or black (or both)

pixels over the image.

198

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38 Cont.

199

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38 Gaussian noise

200

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38 Speckle noise

201

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38 Periodic noise

• If the image signal is subject to a periodic, rather than a random

disturbance, we might obtain an image corrupted by periodic

noise. The effect is of bars over the image. The function

imnoise does not have a periodic option, but it is quite easy to

create our own, by adding a periodic matrix(using a

trigonometric function).

• Salt and pepper noise, Gaussian noise and speckle noise can

all be cleaned by using spatial filtering techniques. Periodic

noise, however, requires the use of frequency domain filtering.

This is because whereas the other forms of noise can be

modelled as local degradations, periodic noise is a global

effect.

202

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38 Cleaning salt and pepper noise

203

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38 Cont.

204

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38 Removal of periodic noise

205

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38 Segmentation

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38 label2rgb

207

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38 bwconncomp

208

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38 Cont.

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38 labelmatrix