MATLAB GRAPHICAL USER INTERFACE (GUI) FOR DIGITAL IMAGE

IMPROVEMENT

HASSAN BIN LEE

Tesis ini dikemukakan kepada

Fakulti Kejuruteraan, Universiti Malaysia Sarawak

Sebagai memenuhi sebahagian daripada syarat

Penganugerahan Sarjana Muda Kejuruteraan

dengan Kepujian (Kejuruteraan Elektronik dan Komputer)

2003

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To my beloved Family

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Acknowledgment

First of all, I would like to dedicate this project to all my family members

especially my parents for their supports, encouragement and love during the period

of this study.

I also would like to take this opportunity to express my gratitude to my

supervisor, Mr. Kismet Hong Ping for being supportive and willing to sacrifice his

precious time in providing guidance and advice to make this project in success.

Special thanks to my colleagues and Ms. Artini for they're opinions and

supports when I encountered the difficulties during the implementation of this

project.

Last but not least, I wish to express my most appreciation to the Faculty of

Engineering, under the excellent leadership of Associate Prof. Dr. Mohammad

Kadim as a Dean, for allowing me to use all kind of resources available in the

laboratory.

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Abstract

The idea of this thesis is to improve the appearance quality of the degraded

digital image. For the improvement purpose, three methods are used such as contract

manipulation, image filtering and geometric transformation. These methods actually

based on the basic methods of image enhancement and restoration in digital image

processing. The MATLAB software is used to develop an application software or

GUI (Graphical User Interface) that enable the user to improve an image easily. The

quality of the output image is usually depends on the viewer judgment. The result is

obtained subjectively and there are no specific standards to characterized the good

image. Therefore, this thesis might be the initial step for more detail research on the

digital image processing where nowadays it becomes the major part of any new

application development.

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Abstrak

Idea asas untuk pembikinan tesis ini adalah untuk memperbaiki kualiti

penampilan sesuatu imej digital yang mungkin terganggu oleh hingar ataupun isyarat

yang tidak dikehendaki. Bagi tujuan memperbaiki kualiti imej digital, terdapat tiga

kaedah yang telah digunakan iaitu manipulasi kontras, penapisan imej dan

transformasi geometri. kaedah-kaedah ini adalah berdasarkan kepada kaedah-kaedah

asas pada peringkat penambahan dan pemulihan imej dalam pemprosessan imej

digital. Perisian MATLAB telah digunakan untuk membangunkan perisian applikasi

ataupun dengan kata lain pengantaramuka grafik untuk pengguna bagi memudahkan

kerja-kerja pembaikian imej digital. Keputusan untuk setiap imej keluaran yang

dihasilkan adalah bergantung kepada pemerhatian pengendali itu sendiri. Ini

bermakna menentukan imej keluaran yang terbaik adalan dibuat secara subjektif dan

tiada piawaian-piawaian tertentu untuk menentukan sama ada imej itu baik atau

tidak. Dengan pembangunan applikasi perisian ini diharap menjadi sebagai satu

langkah permulaan kepada lebih banyak lagi kajian dan pembangunan terhadap

pemprosessan imej digital yang kini semakin diperlukan dalam setiap pembangunan

applikasi.

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TABLE OF CONTENTS

Dedication

Acknowledgment

Abstract

Abstrak

Table of Contents

List of Figures

List of Tables

CHAPTER 1 INTRODUCTION

1.1 Background

1.2 Objectives

1.3 Thesis outline

CHAPTER 2 LITERATURE REVIEW

2.1 Introduction to Digital Image

2.1.1 Image Digitization

2.1.2 Image Resolution

2.2 Introduction to Digital Image Processing

2.2.1 Application of Digital Image Processing

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2.3 Introduction to MATLAB

2.3.1 The MATLAB System

2.4 Image Improvement

2.5 Image Enhancement

2.5.1 Gray-Level Transformations

2.5.1.1 Contrast Stretching

2.5.1.2 Gamma Correction

2.5.2 Histogram Processing

2.5.2.1 Histogram Equalization

2.5.3 Image Noise

2.5.3.1 Gaussian Noise

2.5.3.2 Rayleigh Noise

2.5.3.3 Erlang Noise

2.5.3.4 Exponential Noise

2.5.3.5 Uniform Noise

2.5.3.6 Impulse Noise

2.5.4 Basic of Spatial Filtering

2.5.4.1 Smoothing Spatial Filter

2.5.4.1.1 Gaussian Filter

2.5.4.1.2 Averaging Filter

2.5.4.1.3 Median Filter

2.5.4.2 Sharpening Spatial Filter

2.2.4.2.1 Unsharp Filter

2.6 Image Restoration

2.6.1 Weiner Filter

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2.6.2 Inverse Filter

2.7 Geometric Transformations

2.7.1 Image Rotation

2.7.2 Image Scaling

2.7.3 Gray Level Interpolation

2.7.3.1 Nearest Neighbor Interpolation

2.7.3.2 Linear Interpolation

2.7.3.3 Bicubic Interpolation

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CHAPTER 3 DESIGN AND IMPLEMENTATION 44

3.1 Image Processing in MATLAB

3.2 What is the Image Processing Toolbox

3.3 Images in MATLAB and the Image Processing Toolbox

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3.3.1 Data Types 46

3.3.2 Reading, Writing, Displaying and Image Information 46

3.3.3 Image Types in The Toolbox 47

3.3.3.1 Converting Image to Other Types 53

3.4 Image Histogram and Histogram Equalization 54

3.5 Contrast Manipulation 57

3.6 Image Filtering 58

3.6.1 Filtering using imfilter 58

3.6.2 Filtering using Predefined Filter Types 59

3.6.3 Filtering using medfilt2 61

3.6.4 Filtering using wiener2 62

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3.7 Geometric Manipulation

3.7.1 Interpolation

3.7.2 Image Resizing

3.7.3 Image Cropping

3.7.4 Image Rotation

3.8 MATLAB GUI Design

3.8.1 Creating GUIs with GUIDE

3.8.1.1 GUI Development Environment

3.8.1.2 The Implementation of a GUI

3.8.1.3 GUI Layout Tools

3.8.1.4 Programming GUI

3.9 GUI Implementation

Chapter 4 RESULTS AND DISCUSSIONS

4.1 Intensity Adjustment

4.2 Image Filtering

4.3 Geometric Transformation

CHAPTER 5 RECOMMENDATION AND CONCLUSION

5.1 Recommendation

5.2 Conclusion

REFERANCES

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BIBLIOGRAPHY

APPENDIX A

APPENDIX B

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LIST OF FIGURES

Figure Page

2.1 Digitization of a continuous image 5

2.2 Digitization process 6

2.3 Sampling Process. 6

2.4 Spatial resolutions 8

2.5 Gray level resolutions 8

2.6 Image Enhancement techniques 18

2.7 Contrast stretching transformation function 19

2.8 Contrast stretching 20

2.9 Gamma correction 21

2.10 Image histogram 22

2.11 Four basic image types with their histogram 23

2.12 Histogram equalization 25

2.13 An example of image noise 26

2.14 Convolution mask 30

2.15 2-D Gaussian distribution with mean (0,0) and u=1 32

2.16 Discrete approximation to Gaussian function with o =1.0 32

2.17 3x3 filter mask 3X3 averaging filter often used in averaging filter 33

2.18 3x3 Square neighborhoods and its median value 34

2.19 Unsharp filtering operator. 36

2.20 Geometric Transformations 38

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2.21 Scaling factor of 2 40

2.22 Nearest Neighbor Interpolation 41

2.23 Linear Interpolation 42

3.1 An indexed image called tower jpg 49

3.2 An intensity image called kinabalu jpg 50

3.3 Binary image 51

3.4 Image Histogram 55

3.5 Histogram Equalization 56

3.6 Contrast Manipulation 57

3.7 Filtering an image Using Predefined Filter Type for Gaussian 60

filter

3.8 Median filtering 62

3.9 Applied Weiner filter Using wiener2 function 63

3.10 Image Resizing 66

3.11 Image Cropping 67

3.12 Image Rotation 69

3.13 MATLAB GUI Layout Tools 72

3.14 The flow chart of the image improvement process in the first GUI 75

3.15 MATLAB GUI for the first part 76

3.16 The flow chart of the image improvement process in the second 77

GUI

3.17 MATLAB GUI for the second part

4.1 The result of the intensity adjustment

4.2 Histogram equalization of the image

4.3 The result of noise reduction

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4.4 The rescaled image result

4.5 The result of rotated image

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LIST OF TABLES

Table Page

3.1 Image conversion functions 54

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CHAPTER 1

INTRODUCTION

1.1 Background

The first computer powerful enough to carry out meaningful image processing

tasks appeared in the early 1960s. The birth of digital image processing today can be

traced to the availability of those machines and the onset of the space program

pursuing by United States, National Aeronautics and Space Administration (NASA)

during that period. Work on using computer techniques for improving images from

space probe began at the Jet Propulsion Laboratory (Pasadena, California) in 1964

when pictures of the moon transmitted by Ranger 7. [1)

In parallel with space applications, digital image processing techniques began

in the late 1960s and early 1970s to be used in medical imaging, remote earth

resources observations and astronomy. In 1979, Computerized Tomography (CT)

invented by Sir Godfrey N. Hounsfield and Prof. Allan M. Cormack [11 becomes the

most important element in medical diagnosis.

Digital image processing is a rapidly evolving field with growing applications

in science and engineering. Interest in digital image processing methods stems from

two-principle application areas: [2]

(i) Improvement of pictorial information for human interpretation, and

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(ii) Processing of image data for storage, transmission, and representation

for autonomous machine perception.

In this thesis, the attention will be focused on the digital image enhancement

and restoration techniques. The principle application of this thesis project is to

improve of pictorial information in an image for human interpretation. MATLAB

version 6.1 Released 12.1 software is used to develop an application for image

enhancement and restoration.

1.2 Objectives

Basically, the main idea of this thesis is to develop an application software

using MATLAB to improve the digital image. The objectives of this thesis are as

follows:

i. Apply several methods that can be used to improve the digital image

quality

ii. Analyze the effect of each method when it is applied to an image

iii. Compare several methods according to the quality of the output image

and their performances

iv. To improve the digital image and develop an application software using

MATLAB

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1.3 Thesis outline

This thesis report is divided into five chapters.

Chapter 1 describes the background of the digital image processing. In

addition, this chapter also stated the objectives of the thesis.

Chapter 2 consists of the literature review that is relevant to the thesis. It

includes the explanation of the digital image, image processing and MATLAB that are

important elements for this thesis. The contrast manipulation, image filtering and

geometric transformation were also included.

The implementation of the image improvement using MATLAB and an

application software design and development were described in Chapter 3. The

functions applied in this thesis are obtained directly from MATLAB software. At last

the application software development was illustrated.

Chapter 4 explains the results obtained after testing the application software

that was built.

Lastly, Chapter 5 consists of recommendations on how to improve this thesis

in the future and the conclusion in regards to this thesis.

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CHAPTER 2

LITERATURE REVIEW

2.1 Introduction to Digital Image

A natural image begins as a continuously varying array of shades and colors.

In the case of a photograph, shades vary from light to dark and colors vary from reds

through yellows to blues. An image considered to be a function of two real variables,

for example, a (x, y) with a as the amplitude (e. g. brightness) of the image at the real

coordinate position (x, y). This type of image is called continuous tone image. 131

On the other hand, a digital image a[m, n] described in a 2D discrete space is

derived from an analog image a(x, y) in a 2D continuous space through a sampling

process that is frequently referred to as digitization. The 2D continuous image a(x y)

is divided into N rows and M columns. The intersection of a row and a column is

termed a pixel. The value assigned to the integer coordinates [m nj with {m=0,1,2,...,

M-1} and {n=0,1,2,..., N-1} is a[m, n]. In fact, in most cases a (x, y), which is might

consider to be the physical signal that impinges on the face of a 2D sensor is actually

a function of many variables including depth (z), color (A. ), and time (t).

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Value = a(x, y, z, A, z)

Figure 2.1 Digitization of a continuous image

The image shown in Figure 2.1 has been divided into N= 21 rows and M=

20 columns. The value assigned to every pixel is the average brightness in the pixel

rounded to the nearest integer value. The process of representing the amplitude of the

2D signal at a given coordinate as an integer value with L different gray levels is

usually referred to as amplitude quantization or simply quantization. [41

2.1.1 Image Digitization

The digitization process is used to form a digital image. This process consists

of two subprocesses called sampling and quantization process. The digitization

process is shown in Figure 2.2.

Converting from a continuous image f (x, y) to its digital representation I (i, j)

require discretization of both spatial coordinates and the amplitude (gray level). The

process of discretization of spatial coordinates is called sampling process. Figure 2.3

illustrates the sampling process.

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Continuous Tone Image

Sampled Image

Quantizer

Figure 2.2: Digitization process

Sampled and Quantized

Image

Each dot in Figure 2.3 represents the physical location of each pixel on the

continuous coordinate system where the measurements are made. The value of the

image at that location is further quantized. The value of L is an important parameter

in the sampling process. It determines the density of sampling rate, which determines

the number of pixels in an image.

0,0

Sampler

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V Figure 2.3 Sampling Process.

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Quantization process corresponds to the discretization of the values of pixels.

The number of quantization levels affects the number of bits needed to store the

value of each pixel [5]. For instance, if b bits are used to represent the pixel gray

level, then the total number of gray levels for a an intensity image is given by L=26,

means that for 8 bits per pixel (b=8), the gray level is equal to 256 level.

2.1.2 Image Resolution

The quality of the digital image is directly related to the number of pixels and

lines, along with the range of gray level values in the image known as image

resolution. Image resolution also can be defined as the capability of the digital image

to resolve the elements of the original scene [3]. For digital image, the resolution

characteristics can be divided into two primary parts:

a) The spatial resolution

Describe how many pixels comprise a digital image. For more pixel in the image,

the greater its spatial resolution. The number of pixels in a digital image depends on

how finely of the sample or divides the image into discrete pixels. Figure 2.4 shows

the examples of the spatial resolution in digital image.

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(a) (b)

Image with 320 pixels x 240 lines Image with 40 pixels x30 lines

Figure 2.4 Spatial resolutions

b) Gray level resolution

The concept of gray level resolution addresses how accurately the digital pixel's

gray level can represent the intensity refer to the original image. When the numeric

range of a pixel's gray level is increased, so it is the pixel's gray level resolution.

Figure 2.5 shows the examples of the gray level resolution in digital image.

(a) (b)

8 bits (256 gray levels) image 2 bits (4 gray levels) image

Figure 2.5 Gray level resolutions

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2.2 Introduction to Digital Image Processing

Digital image processing used by means of digital circuit, computer

processors and software to carry out an operation. It is a method of image processing

enabled by the advent of the digital computer. Digital techniques provide precise

implementation of image processing function, along with great flexibility and power

for general-purpose image processing applications.

Digital image processing has become a significantly form of image

processing because of continuing improvements in sophisticated semiconductor

technologies. This has led to computer hardware performance increases and

declining cost. [3J

Digital image processing operations can be broadly grouped into five

fundamental classes:

a) Image Enhancement

Image enhancement is useful in feature extraction, image analysis, and visual

information display. The goal is to accentuate certain image feature for subsequent

analysis or for image display. Examples include contrast and edge enhancement,

pseudocoloring, noise filtering, sharpening and magnifying. The enhancement

process itself does not increase the inherent information content in the data. It simply

emphasizes interactive specified image characteristics. [2]

b) Image Restoration

Image Restoration refers the technique to removal or minimization of known

degradations in an image. This includes deblurring of images degraded by the

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limitations of a sensor or its environment, noise filtering, and correction of geometric

distortion or non-linearity due to sensors. [21

c) Image Analysis

Image analysis operations break an image into discrete objects and then

classify that object using some measurement process. By using image analysis,

quantitative measurements can be made from an image to produce a description of it.

Common image analysis techniques include extraction and description of scene and

overall image feature, automated measurements, and object classification. 131

d) Image Compression

Image compression deals with the technique for reducing the storage required

to save an image, or the bandwidth required to transmit it. It is possible to be done

because most images inherently contain large amounts of redundant information.

Elimination of these redundancies is the goal of image compression techniques. [1

e) Image Synthesis

Image Synthesis is the technique to create images from other images or non-

image data. These techniques are used when a desired image is either physically or

impractical to acquire, or does not exit in a physical form at all. Reconstruction and

visualization of an image are two primary forms of image synthesis techniques. 131

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