Sách An Introduction to Digital Image Processing with Matlab - Alasdair McAndrew

An Introduction to Digital Image
Processing with Matlab
Notes for SCM2511 Image
Processing 1
Semester 1, 2004
Alasdair McAndrew


Contents
1 Introduction 1
1.1 Images and pictures . 1
1.2 What is image processing? . 1
1.3 Image Acquisition and sampling 4
1.4 Images and digital images . 8
1.5 Some applications 10
1.6 Aspects of image processing 11
1.7 An image processing task . 12
1.8 Types of digital images . 12
1.9 Image File Sizes . 14
1.10 Image perception 16
1.11 Greyscale images 17
1.12 RGB Images . 19
1.13 Indexed colour images . 21
1.14 Data types and conversions 23
1.15 Basics of image display . 24
1.16 The imshow function 26
1.17 Bit planes 30
1.18 Spatial Resolution . 30
Exercises 34
2 Point Processing 37
2.1 Introduction . 37
2.2 Arithmetic operations . 38
2.3 Histograms . 42
2.4 Lookup tables 53
Exercises 54
3 Neighbourhood Processing 57
3.1 Introduction . 57
3.2 Notation . 61
3.3 Filtering in Matlab 62
3.4 Frequencies; low and high pass lters . 66
3.5 Edge sharpening 70
3.6 Non-linear lters 76
Exercises 77
4 The Fourier Transform 81
CONTENTS iii
4.1 Introduction . 81
4.2 Background . 81
4.3 The one-dimensional discrete Fourier transform . 81
4.4 The two-dimensional DFT . 85
4.5 Fourier transforms in Matlab 90
4.6 Fourier transforms of images 92
4.7 Filtering in the frequency domain . 96
Exercises 107
5 Image Restoration (1) 109
5.1 Introduction . 109
5.2 Noise . 110
5.3 Cleaning salt and pepper noise 113
5.4 Cleaning Gaussian noise 117
Exercises 121
6 Image Restoration (2) 125
6.1 Removal of periodic noise . 125
6.2 Inverse ltering . 127
6.3 Wiener ltering . 132
Exercises 133
7 Image Segmentation (1) 137
7.1 Introduction . 137
7.2 Thresholding 137
7.3 Applications of thresholding 140
7.4 Adaptive thresholding . 141
Exercises 144
8 Image Segmentation (2) 145
8.1 Edge detection . 145
8.2 Derivatives and edges 145
8.3 Second derivatives . 151
8.4 The Hough transform 155
Exercises 160
9 Mathematical morphology (1) 163
9.1 Introduction . 163
9.2 Basic ideas 163
9.3 Dilation and erosion 165
Exercises 173
10 Mathematical morphology (2) 175
10.1 Opening and closing 175
10.2 The hit-or-miss transform . 180
10.3 Some morphological algorithms 182
Exercises 187
iv CONTENTS
11 Colour processing 191
11.1 What is colour? . 191
11.2 Colour models 195
11.3 Colour images in Matlab . 199
11.4 Pseudocolouring . 202
11.5 Processing of colour images 205
Exercises 211
12 Image coding and compression 215
12.1 Lossless and lossy compression . 215
12.2 Human coding . 215
12.3 Run length encoding 218
Exercises 222
Bibliography 225
Index 226
Chapter 1
Introduction
1.1 Images and pictures
As we mentioned in the preface, human beings are predominantly visual creatures: we rely heavily
on our vision to make sense of the world around us. We not only look at things to identify and
classify them, but we can scan for dierences, and obtain an overall rough feeling for a scene with
a quick glance.
Humans have evolved very precise visual skills: we can identify a face in an instant; we can
dierentiate colours; we can process a large amount of visual information very quickly.
However, the world is in constant motion: stare at something for long enough and it will change
in some way. Even a large solid structure, like a building or a mountain, will change its appearance
depending on the time of day (day or night); amount of sunlight (clear or cloudy), or various shadows
falling upon it.
We are concerned with single images: snapshots, if you like, of a visual scene. Although image
processing can deal with changing scenes, we shall not discuss it in any detail in this text.
For our purposes, 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.