数字图像处理基础：核心技术解析

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"Principles of Digital Image Processing: Fundamental Techniques" 是一本专注于数字图像处理基础技术的教科书，属于Undergraduate Topics in Computer Science（UTiCS）系列，旨在为计算机科学和信息科学领域的本科生提供高质量的教学内容。该系列教材采用简洁、现代的方法，适合自学或一至两个学期的课程使用。本书由权威专家 Wilhelm Burger 和 Mark J. Burge 共同撰写，并经过国际顾问委员会的审阅，包含丰富的示例和习题，许多还附有完整的解答。本书深入探讨了数字图像处理的基本概念和技术，是学习该领域的入门读物。以下是其中可能涵盖的一些关键知识点： 1. **数字图像基础**：介绍数字图像的基本元素，如像素、图像的表示（如灰度图像、彩色图像）、图像的分辨率和位深度等。 2. **图像获取**：讨论图像捕获设备，如数码相机、扫描仪等，以及它们如何将光学信号转换为数字图像。 3. **图像数字化**：包括采样理论和量化过程，阐述奈奎斯特定理在图像采样中的应用，以及量化对图像质量的影响。 4. **图像增强**：介绍各种图像增强技术，如直方图均衡化、空间滤波（低通滤波、高通滤波、锐化滤波）、对比度拉伸等，以改善图像视觉效果。 5. **图像变换**：讲解傅立叶变换在图像处理中的应用，包括离散傅立叶变换（DFT）、快速傅立叶变换（FFT），以及傅立叶域的图像分析和滤波。 6. **图像分割**：介绍用于分离图像中不同区域或对象的技术，如阈值分割、边缘检测（如Canny算法）、区域生长方法等。 7. **几何变换**：包括平移、旋转、缩放等基本变换，以及更复杂的仿射变换和透视变换，这些都是图像校正和配准的基础。 8. **颜色理论**：探讨RGB、CMYK、HSV等颜色模型，以及颜色空间转换的重要性。 9. **图像编码与压缩**：介绍无损和有损压缩方法，如霍夫曼编码、游程编码、JPEG、JPEG 2000等标准及其原理。 10. **图像分析与理解**：涉及特征提取（如角点检测、边缘检测）、模板匹配、物体识别和机器学习在图像分类中的应用。 11. **实践应用**：书中可能涵盖图像处理在医学成像、遥感、安全监控、机器视觉等领域的实际应用案例。通过本书，读者不仅可以掌握数字图像处理的基本原理和技术，还能通过实例和习题来加深理解并提升解决问题的能力。无论是对计算机视觉、图像处理感兴趣的初学者，还是专业研究者，都能从中受益。

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2 1. Digital Images

digital image processing. They are expected to be able to knowledgeably manip-

ulate images and related digital media and, in the same way, software engineers

and computer scientists are increasingly confronted with developing programs,

databases, and related systems that must correctly deal with digital images.

The simple lack of practical experience with this type of material, combined

with an often unclear understanding of its basic foundations and a tendency

to underestimate its diﬃculties, frequently leads to ineﬃcient solutions, costly

errors, and personal frustration.

1.1 Programming with Images

Even though the term “image processing” is often used interchangeably with

that of “image editing”, we introduce the following more precise deﬁnitions.

Digital image editing, or as it is sometimes referred to, digital imaging, is the

manipulation of digital images using an existing software application such as

Adobe Photoshop or Corel Paint Digital image processing, on the other hand,

is the conception, design, development, and enhancement of digital imaging

programs.

Modern programming environments, with their extensive APIs (applica-

tion programming interfaces), make practically every aspect of computing, be

it networking, databases, graphics, sound, or imaging, easily available to non-

specialists. The possibility of developing a program that can reach into an

image and manipulate the individual elements at its very core is fascinating

and seductive. You will discover that with the right knowledge, an image be-

comes ultimately no more than a simple array of values, and that with the right

tools you can manipulate in any way imaginable.

Computer graphics, in contrast to digital image processing, concentrates

on the synthesis of digital images from geometrical descriptions such as three-

dimensional object models [14,16,41]. While graphics professionals today tend

to be interested in topics such as realism and, especially in terms of computer

games, rendering speed, the ﬁeld does draw on a number of methods that

originate in image processing, such as image transformation (morphing), re-

construction of 3D models from image data, and specialized techniques such

as image-based and non-photorealistic rendering [33,42]. Similarly, image pro-

cessing makes use of a number of ideas that have their origin in computational

geometry and computer graphics, such as volumetric (voxel) models in medical

image processing. The two ﬁelds perhaps work closest when it comes to dig-

ital post-production of ﬁlm and video and the creation of special eﬀects [43].

This book provides a thorough grounding in the eﬀective processing of not only

images but also sequences of images; that is, videos.

Digital images are the central theme of this book, and unlike just a few

1.2 Image Acquisition 3

years ago, this term is now so commonly used that there is really no reason to

explain it further. Yet, this book is not about all types of digital images, and

instead it focuses on raster images that are made up of picture elements,more

commonly known as pixels, arranged in a regular rectangular grid.

Every day, people work with a large variety of digital raster images such as

color photographs of people and landscapes, grayscale scans of printed docu-

ments, building plans, faxed documents, screenshots, medical images such as

x-rays and ultrasounds, and a multitude of others (Fig. 1.1). Despite all the

diﬀerent sources for these images, they are all, as a rule, ultimately represented

as rectangular ordered arrays of image elements.

1.2 Image Acquisition

The process by which a scene becomes a digital image is varied and complicated,

and, in most cases, the images you work with will already be in digital form,

so we only outline here the essential stages in the process. As most image

acquisition methods are essentially variations on the classical optical camera,

we will begin by examining it in more detail.

1.2.1 The Pinhole Camera Model

The pinhole camera is one of the simplest camera models and has been in use

since the 13th century, when it was known as the “Camera Obscura”. While

pinhole cameras have no practical use today except to hobbyists, they are a

useful model for understanding the essential optical components of a simple

camera.

The pinhole camera consists of a closed box with a small opening on the

front side through which light enters, forming an image on the opposing wall.

The light forms a smaller, inverted image of the scene (Fig. 1.2).

Perspective transformation

The geometric properties of the pinhole camera are very simple. The optical

axis runs through the pinhole perpendicular to the image plane. We assume a

visible object (the cactus in Fig. 1.2) located at a horizontal distance Z from

the pinhole and vertical distance Y from the optical axis. The height of the

projection y is determined by two parameters: the (ﬁxed) depth of the camera

box f and the distance Z of the object from the origin of the coordinate system.

By matching similar triangles we obtain the relations

y = −f

and x = −f

(1.1)

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