MATLAB
GRAPHICAL
USER INTERFACE
(GUI) FOR DIGITAL
IMPROVEMENT
HASSAN BIN LEE
Tesis ini dikemukakan Fakulti Kejuruteraan,
Universiti
kepada Malaysia Sarawak
Sebagai memenuhi sebahagian daripada syarat Penganugerahan dengan Kepujian
Sarjana Muda Kejuruteraan
(Kejuruteraan
2003
1
Elektronik
dan Komputer)
IMAGE
To my beloved Family
11
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 difficulties I the encountered when supports
for they're opinions and
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.
111
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 development. application
iv
Abstrak
Idea penampilan
asas untuk
pembikinan
sesuatu imej digital yang mungkin
yang tidak dikehendaki. kaedah
transformasi
asas pada peringkat
iaitu
kaedah-kaedah
geometri.
penambahan
dihasilkan
adalah
imej
bermakna menentukan
Dengan
langkah
pemprosessan
dan
kepada kaedah-kaedah
dalam
pemprosessan
untuk membangunkan
imej
perisian applikasi
grafik untuk pengguna bagi memudahkan
Keputusan
kepada
untuk
setiap imej
keluaran itu
yang Ini
imej keluaran yang terbaik adalan dibuat secara subjektif
dan
tertentu
pembangunan
permulaan
imej
imej
penapisan
sendiri.
tiada piawaian-piawaian tidak.
kualiti
memperbaiki
imej digital, terdapat tiga
kontras,
dan pemulihan
digital.
bergantung
kualiti
ini adalah berdasarkan
ataupun dengan kata lain pengantaramuka pembaikian
untuk
terganggu oleh hingar ataupun isyarat
manipulasi
telah digunakan
Perisian MATLAB
kerja-kerja
adalah
Bagi tujuan memperbaiki
digunakan
telah
yang
digital.
tesis ini
kepada
imej digital
untuk
applikasi lebih
pengendali
pemerhatian
menentukan perisian
sama ada imej
ini diharap
lagi kajian
banyak
yang kini semakin diperlukan
applikasi.
V
menjadi
itu baik
atau
sebagai satu
dan pembangunan
terhadap
dalam setiap pembangunan
TABLE OF CONTENTS
Page
Dedication
ii
Acknowledgment
111
Abstract
iv
Abstrak
V
Table of Contents
V1
List of Figures
XI
List of Tables
xiv
CHAPTER
1 INTRODUCTION
I
1.1 Background
1
1.2 Objectives
2
1.3 Thesis outline
3
CHAPTER
2 LITERATURE
4
REVIEW
4
2.1 Introduction to Digital Image 2.1.1 Image Digitization
5
2.1.2 Image Resolution
7
2.2 Introduction to Digital Image Processing 2.2.1 Application of Digital Image Processing
V1
9 11
2.3 Introduction to MATLAB 2.3.1 The MATLAB
13
System
14
2.4 Image Improvement
16
2.5 Image Enhancement
16
2.5.1 Gray-Level Transformations
18
2.5.1.1 Contrast Stretching
18
2.5.1.2 Gamma Correction
20
2.5.2 Histogram Processing
21
2.5.2.1 Histogram Equalization 2.5.3 Image Noise
24 25
2.5.3.1 Gaussian Noise
26
2.5.3.2 Rayleigh Noise
27
2.5.3.3 Erlang Noise
27
2.5.3.4 Exponential Noise
28
2.5.3.5 Uniform Noise
28
2.5.3.6 Impulse Noise
28
2.5.4 Basic of Spatial Filtering
29
2.5.4.1 Smoothing Spatial Filter
31
2.5.4.1.1 Gaussian Filter
31
2.5.4.1.2 Averaging Filter
33
2.5.4.1.3 Median Filter
34
2.5.4.2 Sharpening Spatial Filter 2.2.4.2.1 Unsharp Filter
35 35
36
2.6 Image Restoration 2.6.1 Weiner Filter
37
vii
2.6.2 Inverse Filter
37
2.7 Geometric Transformations
38
2.7.1 Image Rotation
39
2.7.2 Image Scaling
40
2.7.3 Gray Level Interpolation
40
CHAPTER
2.7.3.1 Nearest Neighbor Interpolation
41
2.7.3.2 Linear Interpolation
42
2.7.3.3 Bicubic Interpolation
43
3 DESIGN AND IMPLEMENTATION
44
3.1
Image Processing in MATLAB
44
3.2
What is the Image Processing Toolbox
44
3.3
Images in MATLAB
45
and the Image Processing Toolbox
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
viii
3.7
3.8
3.9
Geometric Manipulation
64
3.7.1
Interpolation
64
3.7.2
Image Resizing
65
3.7.3
Image Cropping
66
3.7.4
Image Rotation
67
MATLAB
GUI Design
69
3.8.1 Creating GUIs with GUIDE
70
3.8.1.1 GUI Development Environment
70
3.8.1.2 The Implementation of a GUI
71
3.8.1.3 GUI Layout Tools
71
3.8.1.4 Programming GUI
72
GUI Implementation
Chapter 4 RESULTS
73
AND DISCUSSIONS
80
4.1 Intensity Adjustment
80
4.2 Image Filtering
82
4.3 Geometric Transformation
83
CHAPTER
5 RECOMMENDATION
AND CONCLUSION
87
5.1 Recommendation
87
5.2 Conclusion
89
REFERANCES
90
ix
BIBLIOGRAPHY APPENDIX
A
APPENDIX
B
92
X
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
X1
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
3.14
The flow chart of the image improvement process in the first GUI
3.15
MATLAB
3.16
The flow chart of the image improvement process in the second
72
GUI Layout Tools
GUI for the first part
75 76 77
GUI GUI for the second part
78
3.17
MATLAB
4.1
The result of the intensity adjustment
81
4.2
Histogram equalization of the image
82
4.3
The result of noise reduction
84
xii
4.4
The rescaled image result
85
4.5
The result of rotated image
86
xiii
LIST OF TABLES
Table
3.1
Page
Image conversion functions
xiv
54
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 began Propulsion (Pasadena, California) in Jet Laboratory 1964 the at space probe 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 diagnosis. in important medical element most
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
I
Processing of image data for storage, transmission, and representation
(ii)
for autonomous machine perception.
In this thesis, the attention will be focused on the digital image enhancement and restoration techniques. The principle improve of pictorial
information
application of this thesis project is to
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.
Objectives
1.2
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
2
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
included. transformation were also geometric
The implementation
improvement image the of
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.
3
CHAPTER 2
LITERATURE
2.1
Introduction
REVIEW
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 image is (x, This type of position called continuous tone image. 131 y). coordinate
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 is impinges face 2D be that the the to actually sensor on of a physical signal consider ), and time (t). a function of many variables including depth (z), color (A.
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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 integer to the value. The process of representing the amplitude of the nearest rounded 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
in 2.2. is Figure process shown Converting from a continuous image f (x, y) to its digital representation I (i, j) The level). both (gray discretization the amplitude of spatial coordinates and require 2.3 is Figure discretization of spatial coordinates called process. of sampling process illustrates the sampling process.
5
Quantizer
Sampler
Sampled and Quantized Image
Sampled Image
Continuous Tone Image
Figure 2.2: Digitization process
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.
1
0,0
ºX
1
V Figure 2.3 Sampling Process.
6
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 into be divided two primary parts: can characteristics
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.
7
(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
8
2.2
Introduction
to Digital Image Processing
image processing used by means of digital
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
processing because of continuing technologies.
improvements
form
of image
in sophisticated semiconductor
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 include for image display. Examples contrast and edge enhancement, or analysis pseudocoloring,
noise filtering,
sharpening and magnifying.
The enhancement
increase inherent information in data. It simply itself does the the content not process 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
9
by images degraded the of
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 nonimage 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 forms 131 image image techniques. two primary of synthesis are visualization of an
10
