冈萨雷斯《数字图像处理》第三版第一章

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"冈萨雷斯的数字图像处理英文原版第一章，是数字图像处理领域的经典教材。本书由Rafael C. Gonzalez和Richard E. Woods合作撰写，由Pearson Education, Inc.出版，属于第三版。内容涵盖了数字图像处理的基础概念和技术，但不包含彩色图片。" 在数字图像处理这一领域，冈萨雷斯和伍兹合著的《Digital Image Processing》是一本具有广泛影响力的教材。这本书的第三版深入浅出地介绍了数字图像处理的各个方面，包括基本理论、算法和应用。第一章通常会为读者奠定基础，引导他们进入这个复杂的主题。第一章可能会涵盖以下关键知识点： 1. **数字图像的基本概念**：介绍图像的定义，图像的类型（如灰度图像、彩色图像），以及数字图像与模拟图像的区别。数字图像由像素阵列构成，每个像素都有特定的数值表示其亮度或颜色。 2. **图像的表示与存储**：讨论像素的编码方式，如二进制、灰度级或RGB颜色模型。此外，还会涉及图像的分辨率，即每英寸像素数量（PPI）和图像大小。 3. **图像的获取**：介绍不同的图像获取设备，如数码相机、扫描仪等，以及它们的工作原理。 4. **图像的基本操作**：包括图像的平移、缩放、旋转等几何变换，以及直方图、对比度和亮度调整等图像增强技术。 5. **数字图像的数学基础**：可能涉及傅立叶变换在图像处理中的应用，它是理解和分析图像频域特性的重要工具。 6. **图像的数字化过程**：阐述采样和量化的过程，这两个步骤是将模拟图像转换为数字图像的关键。 7. **图像的文件格式**：介绍常见的数字图像文件格式，如JPEG、PNG、TIFF等，以及它们的优缺点。虽然这个版本没有包含彩图，但对黑白图像的处理原理同样重要，因为许多基础概念和算法在单色图像上同样适用。学习这一章的内容，读者可以建立起数字图像处理的基本框架，并为进一步深入学习打下坚实基础。对于IT专业人士，尤其是从事图像处理、计算机视觉或者相关领域研究的人来说，这是一份不可或缺的学习资料。

1.2 ■ The Origins of Digital Image Processing 5

be used for digital image processing. Briefly, these advances may be summa-

rized as follows: (1) the invention of the transistor at Bell Laboratories in 1948;

(2) the development in the 1950s and 1960s of the high-level programming lan-

guages COBOL (Common Business-Oriented Language) and FORTRAN

(Formula Translator); (3) the invention of the integrated circuit (IC) at Texas

Instruments in 1958; (4) the development of operating systems in the early

1960s; (5) the development of the microprocessor (a single chip consisting of

the central processing unit, memory, and input and output controls) by Intel in

the early 1970s; (6) introduction by IBM of the personal computer in 1981; and

(7) progressive miniaturization of components, starting with large scale integra-

tion (LI) in the late 1970s, then very large scale integration (VLSI) in the 1980s,

to the present use of ultra large scale integration (ULSI). Concurrent with

these advances were developments in the areas of mass storage and display sys-

tems, both of which are fundamental requirements for digital image processing.

The first computers powerful enough to carry out meaningful image pro-

cessing tasks appeared in the early 1960s. The birth of what we call digital

image processing today can be traced to the availability of those machines and

to the onset of the space program during that period. It took the combination

of those two developments to bring into focus the potential of digital image

processing concepts. Work on using computer techniques for improving im-

ages from a space probe began at the Jet Propulsion Laboratory (Pasadena,

California) in 1964 when pictures of the moon transmitted by Ranger 7 were

processed by a computer to correct various types of image distortion inherent

in the on-board television camera. Figure 1.4 shows the first image of the

moon taken by Ranger 7 on July 31, 1964 at 9:09 A.M. Eastern Daylight Time

(EDT), about 17 minutes before impacting the lunar surface (the markers,

called reseau marks, are used for geometric corrections, as discussed in

Chapter 2). This also is the first image of the moon taken by a U.S. spacecraft.

The imaging lessons learned with Ranger 7 served as the basis for improved

methods used to enhance and restore images from the Surveyor missions to

the moon, the Mariner series of flyby missions to Mars, the Apollo manned

flights to the moon, and others.

FIGURE 1.4

The

first picture of the

moon by a U.S.

spacecraft. Ranger

7 took this image

on July 31, 1964 at

9:09

A.M. EDT,

about 17 minutes

before impacting

the lunar surface.

(Courtesy of

NASA.)

GONZ_CH01v5.qxd 7/10/07 11:57 AM Page 5

6 Chapter 1 ■ Introduction

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. The invention in the early 1970s

of computerized axial tomography (CAT), also called computerized tomogra-

phy (CT) for short, is one of the most important events in the application of

image processing in medical diagnosis. Computerized axial tomography is a

process in which a ring of detectors encircles an object (or patient) and an

X-ray source, concentric with the detector ring, rotates about the object. The

X-rays pass through the object and are collected at the opposite end by the

corresponding detectors in the ring.As the source rotates, this procedure is re-

peated. Tomography consists of algorithms that use the sensed data to con-

struct an image that represents a “slice” through the object. Motion of the

object in a direction perpendicular to the ring of detectors produces a set of

such slices, which constitute a three-dimensional (3-D) rendition of the inside

of the object. Tomography was invented independently by Sir Godfrey

N. Hounsfield and Professor Allan M. Cormack, who shared the 1979 Nobel

Prize in Medicine for their invention. It is interesting to note that X-rays were

discovered in 1895 by Wilhelm Conrad Roentgen, for which he received the

1901 Nobel Prize for Physics. These two inventions, nearly 100 years apart, led

to some of the most important applications of image processing today.

From the 1960s until the present, the field of image processing has grown

vigorously. In addition to applications in medicine and the space program, dig-

ital image processing techniques now are used in a broad range of applica-

tions. Computer procedures are used to enhance the contrast or code the

intensity levels into color for easier interpretation of X-rays and other images

used in industry, medicine, and the biological sciences. Geographers use the

same or similar techniques to study pollution patterns from aerial and satellite

imagery. Image enhancement and restoration procedures are used to process

degraded images of unrecoverable objects or experimental results too expen-

sive to duplicate. In archeology, image processing methods have successfully

restored blurred pictures that were the only available records of rare artifacts

lost or damaged after being photographed. In physics and related fields, com-

puter techniques routinely enhance images of experiments in areas such as

high-energy plasmas and electron microscopy. Similarly successful applica-

tions of image processing concepts can be found in astronomy, biology, nuclear

medicine, law enforcement, defense, and industry.

These examples illustrate processing results intended for human interpreta-

tion. The second major area of application of digital image processing tech-

niques mentioned at the beginning of this chapter is in solving problems dealing

with machine perception. In this case, interest is on procedures for extracting

from an image information in a form suitable for computer processing. Often,

this information bears little resemblance to visual features that humans use in

interpreting the content of an image. Examples of the type of information used

in machine perception are statistical moments, Fourier transform coefficients,

and multidimensional distance measures.Typical problems in machine percep-

tion that routinely utilize image processing techniques are automatic character

recognition, industrial machine vision for product assembly and inspection,

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