Matlab引导的数字图像处理基础教程
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"《数字图像处理入门 An Introduction To Digital Image Processing With Matlab》是Alasdair McA（麦安德鲁）撰写的一本面向初学者的图像处理书籍，通过使用Matlab进行实践教学，内容深入浅出。书中涵盖了图像处理的基础概念、技术以及应用，旨在帮助读者理解并掌握图像处理的基本原理和方法。"
该书详细介绍了以下几个关键知识点：
1. **图像与图片**: 区分了图像与我们日常所见的图片的概念，强调了图像在科学和工程领域的含义。
2. **什么是图像处理**: 解释了图像处理的定义，包括对图像进行分析、变换和增强等操作，以获取更多信息或改善视觉效果。
3. **图像获取与采样**: 讨论了如何从现实世界获取图像，并介绍了图像采样的重要性，以及采样率和分辨率之间的关系。
4. **数字图像**: 描述了数字图像的构成，包括像素、位深度和图像数据表示。
5. **应用领域**: 提到了图像处理在医学、遥感、安全监控、计算机视觉等多个领域的广泛应用。
6. **图像处理的方面**: 涵盖了图像处理的主要任务，如图像增强、分割、识别和压缩等。
7. **一个图像处理任务**: 通过实例展示了图像处理的完整流程，帮助读者理解实际操作。
8. **数字图像类型**: 包括灰度图像、RGB图像和索引颜色图像的特性及其转换。
9. **图像文件大小**: 解释了图像文件大小与图像分辨率、位深度的关系。
10. **图像感知**: 讨论了人类视觉系统对图像的感知，以及它如何影响图像处理结果。
11. **灰度图像**: 详述了单通道图像，即灰度图像的处理方法和重要性。
12. **RGB图像**: 介绍了红绿蓝三原色模型，以及如何组合这些颜色来表示彩色图像。
13. **索引颜色图像**: 讲解了使用颜色查找表的图像表示方式。
14. **数据类型与转换**: 说明了在处理图像时，不同数据类型（如整型、浮点型）的选择和转换。
15. **图像显示基础**: 介绍如何在Matlab中显示图像，特别提到了`imshow`函数的用法。
16. **位平面**: 解释了位平面的概念，它是理解图像和颜色深度的重要工具。
17. **空间分辨率**: 讨论了图像的空间分辨率，即图像中的细节程度。
书中后续章节还涵盖了点处理（如算术运算和直方图）、邻域处理（包括滤波和边缘检测）等内容，提供了丰富的练习题以加深理解和应用。对于希望学习图像处理和使用Matlab的人来说，这本书是一个理想的起点。
12 CHAPTER 1. INTRODUCTION
removing noise.
Image restoration. This may be considered as reversing the damage done to an image by a
known cause, for example:
removing of blur caused by linear motion,
removal of optical distortions,
removing periodic interference.
Image segmentation. This involves subdividing an image into constituent parts, or isolating
certain aspects of an image:
finding lines, circles, or particular shapes in an image,
in an aerial photograph, identifying cars, trees, buildings, or roads.
These classes are not disjoint; a given algorithm may be used for both image enhancement or for
image restoration. However, we should be able to decide what it is that we are trying to do with
our image: simply make it look better (enhancement), or removing damage (restoration).
1.7 An image processing task
We will look in some detail at a particular real-world task, and see how the above classes may be
used to describe the various stages in performing this task. The job is to obtain, by an automatic
process, the postcodes from envelopes. Here is how this may be accomplished:
Acquiring the image. First we need to produce a digital image from a paper envelope. This an
be done using either a CCD camera, or a scanner.
Preprocessing. This is the step taken before the “major” image processing task. The problem here
is to perform some basic tasks in order to render the resulting image more suitable for the job
to follow. In this case it may involve enhancing the contrast, removing noise, or identifying
regions likely to contain the postcode.
Segmentation. Here is where we actually “get” the postcode; in other words we extract from the
image that part of it which contains just the postcode.
Representation and description. These terms refer to extracting the particular features which
allow us to differentiate between objects. Here we will be looking for curves, holes and corners
which allow us to distinguish the different digits which constitute a postcode.
Recognition and interpretation. This means assigning labels to objects based on their descrip-
tors (from the previous step), and assigning meanings to those labels. So we identify particular
digits, and we interpret a string of four digits at the end of the address as the postcode.
1.8 Types of digital images
We shall consider four basic types of images:
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