课程代码

045101133

课程名称

数字图像处理

英文名称

Digital Image Processing

课程类别

学科领域课

课程性质

选修

学时

总学时：32；实验学时：8；实践学时：20（学生自己利用课外时间）；

学分

2

开课学期

5和7

开课单位

计算机科学与工程学院

适用专业

计算机科学与技术（含全英创新班和联合班）、信息安全等专业

授课语言

中文

先修课程

高级语言程序设计、数据结构

毕业要求（专业培养能力）

1. 设计/开发解决方案：能够设计针对计算机工程复杂问题的解决方案，设计满足特定需求的计算机软硬件系统，并能够在设计环节中体现创新意识，考虑社会、健康、安全、法律、文化以及环境等因素。

2. 研究：能够基于科学原理并采用科学方法对与计算机相关复杂工程问题进行研究，包括设计实验、分析与解释数据、并通过信息综合得到合理有效的结论。

3. 使用现代工具：能够针对与计算机相关复杂工程问题，开发、选择与使用恰当的技术、资源、现代工程工具和信息技术工具，包括对复杂工程问题的预测与模拟，并能够理解其局限性。

课程培养学生的能力（教学目标）

1. 掌握数字图像处理的基本概念、基本原理和实现方法和实用技术

2. 了解数字图像处理基本应用和当前国内外的发展方向。

3. 要求学生通过该课程学习，具备解决应用问题的初步能力，为在计算机视觉、模式识别等领域从事研究与工程研发打下扎实的理论基础。

4. 具备设计简单图像处理系统的能力。

课程简介

本课程是计算机专业本科学生的一门专业选修课。本课程主要讲授数字图像处理的形成与发展、空间域图像增强、频率域图像增强、图像复原、彩色图像处理、图像重建等内容。通过本课程的理论与实践学习，使学生掌握数字图像处理的基本概念、原理和算法，熟练使用数字图像处理编程的基本工具，掌握数字图像处理的算法设计方法，了解数字图像处理的发展和应用以及当前国际国内研究的热点和重要成果及其工程应用前景。为在计算机视觉、模式识别等领域从事研究与开发打下扎实的理论基础。本课程除要求学生完成一般实验外，还需要完成一个大作业。

教学内容与学时分配

1. 绪论

1.1 数字图像处理的概念

1.2 数字图像处理的起源

1.3 数字图像处理的基本步骤

1.4 数字图像处理的应用

重点难点：图像与图形的区别

学习要求：理解数字图像处理的基本概念、应用领域。

学时：2学时

2. 数字图像处理基础

2.1视觉感知要素

2.2 光和电磁波波谱

2.3 图像感知与获取

2.4 图像采样和量化

2.5 像素间的基本关系

重点难点：视觉的适应性

学习要求：了解数字图像的形成；学习视觉和光谱学的基础知识

学时：4学时

3. 空间域图像增强

3.1 背景知识

3.2 基本灰度变换

3.3 直方图处理

3.4 算术逻辑操作

3.5 空间滤波基础

3.6 平滑空间滤波器

3.7 锐化空间滤波器

3.8 混合空间滤波器

重点难点：图像滤波

学习要求：掌握灰度变换、直方图、算术逻辑操作等技术进行图像增强的方法；掌握平滑和锐化滤波方法。

学时：4学时

4. 频率域图像增强

4.1 背景知识

4.2 傅里叶变换和频率域

4.3低通滤波器

4.4 高通滤波器

4.5 同态滤波

重点难点：傅里叶变换和频域

学习要求：了解傅里叶变换和频率域的概念，掌握低通和高通滤波器的原理；掌握同态滤波器的基本原理。

学时：4学时

5. 图像复原

5.1 图像退化模型

5.2 噪声模型

5.3 只存在噪声的图像复原

5.4 频率域消弱周期噪声

5.5 线性位置不变的退化

5.6 估计退化函数

5.7 逆滤波

5.8 维纳滤波

5.9 最小均方滤波

5.10 几何均值滤波

5.11 几何变换

重点难点：图形退化原理

学习要求：理解图像退还的原因并建立退化模型；掌握噪声滤波的方法；学习估计退化函数的一般方法；掌握几种基本的图像复原方法。

学时：4学时

6. 彩色图像处理

6.1 颜色基础

6.2 颜色模型

6.3 伪彩色图像处理

6.4 全彩色图像处理

6.4 彩色变换

6.5 彩色图像平滑和锐化

6.6 彩色分割

重点难点：全彩色图像处理

学习要求：了解彩色的基本概念和几种常用的颜色模型；了解彩色图像处理的方法，掌握彩色图像平滑和锐化方法。

学时：4学时

7. 图像重建基础

7.1 图像重建的概念

7.2 图像重建的历史和应用

7.3图像重建的基本原理

7.4傅里叶变换图像重建

重点难点：雷顿变换

1. 学习要求：了解图像重建的概念，掌握图像重建的原理；

掌握傅里叶变换图像重建的方法。

学时：2学时

实验教学（包括上机学时、实验学时、实践学时）

（1）数字图像读取和颜色、灰度、对比度变化（1学时）

（2）实验二：数字图像灰度变换和伽马校正（1学时）

（3）实验三：数字图像平滑和锐化（2学时）

（4）实验四：频率域低通和高通滤波（1学时）

（5）实验五：数字图像复原（1学时）

（6）实验六：人脸皮肤颜色分层（2学时）

课程论文（大作业，20学时）：公布一系列大作业题目供学生选择，3人一组完成选定的大作业，组内学生各有分工，提高学生的动手能力和实践能力，使学生能够学以致用，具备进行数字图像处理的算法设计和系统设计能力，学习如何撰写论文，为进一步的研究打下扎实的基础。

教学方法

课程教学以课堂教学、课外作业、综合讨论、网络以及授课教师的科研项目等共同实施。

考核方式

本课程注重过程考核，成绩比例为：

平时课堂表现：20%

实验：20%

课程论文（大作业）：60%

教材及参考书

教材：

数字图像处理(第三版) ， R.C.Gonzalez，阮秋琦等译，电子工业出版社，2018.01

参考资料：

[1] 数字图像处理，K.R.Castleman,朱志刚等译，电子工业出版社，2009.

[2]数字图像处理，贾永红，西安电子科技大学出版社，2015

制定人及制定时间

张星明， 2018年4月

Course Code

045101133

Course Title

Digital Image Processing

Course Category

Specialty- related Courses

Course Nature

Elective Course

Class Hours

32

Credits

2

Semester

5 and 7

Institute

School of Computer Science and Engineering

Program Oriented

Computer science and technology, information security

Teaching Language

Chinese

Prerequisites

Advanced Language Programming(C++), Data Structure

Student Outcomes

 (Special Training Ability)

1. Research: An ability to conduct investigations on complex network engineering problems based on scientific theories and by adopting scientific methods, including design of experiments, analysis and interpretation of data, and synthesis of information, to obtain effective conclusions.

2. Applying Modern Tools: An ability to develop, select and apply appropriate techniques, resources, and modern engineering and IT tools for complex network engineering problems, including prediction and modeling of complex engineering problems with an understanding of the limitations.

Teaching Objectives

(1)Master the basic concepts, basic principles and implementation methods and practical technology on digital image processing

(2)Understand the basic application of digital image processing and the current domestic and international development direction on digital image processing.

(3)With the initial ability to solve application problems, and have solid theoretical basis in the field of computer vision, pattern recognition.

(4)Have the ability to design a simple image processing system.

Course Description

This course is a professional course for undergraduates majoring in computer science. This course mainly teaches the formation and development of digital image processing, spatial domain image enhancement, frequency domain image enhancement, image restoration, color image processing, image reconstruction and so on. Through the theory and practice of this course, students can master the basic concepts, principles and algorithms of digital image processing, master the basic tools of digital image processing programming, master the digital image processing algorithm design method, understand the development and application of digital image processing As well as the current international and domestic research achievements and its engineering application prospects. For the computer vision, pattern recognition and other fields engaged in research and development to lay a solid theoretical foundation. In addition to requiring students to complete the general experiment, but also need to complete a Course Design.

Teaching Content and Class Hours Distribution

Chapter 1 Introduction(2 hours)

1.1 Concept of Digital Image Processing

1.2 The Origin of Digital Image Processing

1.3 The Basic Steps of Digital Image Processing

1.4 The Application of Digital Image Processing

Contents: Understand the basic concepts of digital image processing, application areas.

Key points: The difference between image and graphics

Chapter 3 Spatial Domain Image Enhancement(4 hours)

3.1 Background knowledge

3.2 Basic gray scale transformation

3.3 Histogram processing

3.4 Arithmetic logic operation

3.5 Space filter foundation

3.6 Smoothing filter in space domain

3.7 Sharpening spatial filter

3.8 Mixed space filter

Contents: Master the gray-scale transformation, histogram, arithmetic and logic operations and other techniques to enhance the image method; master smooth and sharpening filter method.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      

Key points: Image Filter.

Chapter 4 Frequency domain image enhancement (4 hours)

4.1 Background knowledge

4.2 Fourier transform and frequency domain

4.3 Low-pass filter

4.4 High-pass filter

4.5 Homomorphic filter

Contents: Understand the concept of Fourier transform and frequency domain, master the principle of low-pass and high-pass filter; master the basic principle of homomorphic filter.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     

Key points:Fourier transform and frequency domain.

Chapter 5 Image Restoration (4 hours)

5.1 Image degradation model

5.2 Noise model

5.3 Image restoration for noise

5.4 The Periodic noise

5.5 Degeneration of linear position independent

5.6 Estimate the degradation function

5.7 Inverse filtering

5.8 Wiener filter

5.9 Least mean square filter

5.10 Geometric mean filter

5.11 Geometric transformation

Contents: Understand the reasons for image degradation and establish a degenerate model; master the method of noise filtering; learn the general method of estimating the degenerate function; master some basic image restoration methods.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     

Key points: Image degradation principle.

Chapter 6 Color Image Processing (4 hours)

6.1 Color foundation

6.2 Color model

6.3 Pseudo - color image processing

6.4 Color image processing

6.5 Color transformation

6.6 Color image smoothing and sharpening

6.7 Color segmentation

Contents: Understand the basic concepts of color and several common color model; understand the color image processing methods, master the color image smoothing and sharpening method.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    

Key points: Color image processing.

Chapter 7 Image Reconstruction Foundation (4 hours)

7.1 Concept of image reconstruction

7.2 The history and application of image reconstruction

7.3 The basic principle of image reconstruction

7.4 Fourier transform based image reconstruction

Contents: Understand the basic concepts of color and several common color model; understand the color image processing methods, master the color image smoothing and sharpening method.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    

Key points: Randon transform.

Experimental Teaching

（1）Digital image reading and color, grayscale, contrast conversion，1 hour

（2）Digital image grayscale transformation and gamma correction，1 hour

（3）Digital image smoothing and sharpening, 2 hours

（4）Frequency domain low pass and high pass filtering, 1 hours

（5）Image Restoration, 1 hours

（6）Face skin color segmentation, 2 hours

（7） Course papers (Course project, 20 hours): published a series of course design topics for students to choose, a group of three students completed a course design topics, improve students' practical ability.

Teaching Method

Course is not only taught with class and tutorial but also discussion and research project.

Examination Method

Class participation: 20%

Quiz and Experiment: 20%

Course Paper(Course project): 60%

Teaching Materials and Reference Books

Textbook:

R.C.Gonzalez, Yuan Qiuqi（translate）, Digital Image Processing, Second edition, Tsinghua university Press, 2014

Reference:

[1]K.R.Castleman，Zhu Zhigang（translate）, Digital Image Processing, Electronic Industry Press，2012.

[2]Jia Yonghong, Digital Image Processing, Xi'an University of Electronic Science and Technology Press，2015.

课程代码

045101212

课程名称

数字图像处理

英文名称

Digital Image Processing

课程类别

学科领域课

课程性质

选修

学时

总学时：32；实验学时：8；实践学时：20（学生自己利用课外时间）；

学分

2

开课学期

5和7

开课单位

计算机科学与工程学院

适用专业

计算机科学与技术（含全英创新班和联合班）、信息安全等专业

授课语言

中文授课

先修课程

高级语言程序设计、数据结构

毕业要求（专业培养能力）

（1）研究：能够基于科学原理并采用科学方法对与计算机相关复杂工程问题进行研究，包括设计实验、分析与解释数据、并通过信息综合得到合理有效的结论。

（2）使用现代工具：能够针对与计算机相关复杂工程问题，开发、选择与使用恰当的技术、资源、现代工程工具和信息技术工具，包括对复杂工程问题的预测与模拟，并能够理解其局限性。

课程培养学生的能力（教学目标）

1. 掌握数字图像处理的基本概念、基本原理和实现方法和实用技术

2. 了解数字图像处理基本应用和当前国内外的发展方向。

3. 要求学生通过该课程学习，具备解决应用问题的初步能力，为在计算机视觉、模式识别等领域从事研究与工程研发打下扎实的理论基础。

4. 具备设计简单图像处理系统的能力。

课程简介

本课程是计算机专业本科学生的一门专业选修课。本课程主要讲授数字图像处理的形成与发展、空间域图像增强、频率域图像增强、图像复原、彩色图像处理、图像重建等内容。在课程讲授过程中，充分利用我国在卫星图像、无人机图像、公共安全图像等方面的案例和老一辈科学家的奉献精神，培养学生的家国情怀。通过本课程的理论与实践学习，使学生掌握数字图像处理的基本概念、原理和算法，熟练使用数字图像处理编程的基本工具，掌握数字图像处理的算法设计方法，了解数字图像处理的发展和应用以及当前国际国内研究的热点和重要成果及其工程应用前景。为在计算机视觉、模式识别等领域从事研究与开发打下扎实的理论基础。本课程除要求学生完成一般实验外，还需要完成一个大作业。

主要仪器设备与软件

计算机

实验报告

独立完成实验，每项实验一份报告。

考核方式

现场检查，实验报告检查。

教材、实验指导书及教学参考书目

[1]数字图像处理(第二版) ， R.C.Gonzalez，阮秋琦等译，电子工业出版社，2017.01

[2] 张星明.《数字图像实验指导书》. 自编. 2019.

制定人及发布时间

张星明，2019年5月4日

实验项目编号

实验项目名称

实验学时

实验内容提要

实验类型

实验要求

每组人数

主要仪器设备与软件

1

数字图像读取及色彩、亮度、对比度变化

1

了解数字图像的存储格式，并学会对图像的某些视觉特征作简单处理。从最常用的“.BMP”图像格式中读取图像数据；对数字图像的表示方式（如RGB、YUV）及各种表示方式之间的转换有初步了解；根据输入参数改变数字图像的色彩、亮度、对比度。

验证型

必做

1

计算机，C++和MATLAB软件

2

数字图像灰度变换和伽马校正

1

了解数字图像的灰度反变换和g校正的基本原理。学生自己编程实现图像反转；对数和指数变换；分段线性变换和伽马校正等图像增强算法。

验证型

必做

1

计算机，C++和MATLAB软件

3

数字图像平滑和锐化

2

学会用滤波器去除图像中的噪声。并编程实现：用均值滤波器去除图像中的噪声；用中值滤波器去除图像中的噪声；并比较两种方法的处理结果。理解图像的空间域锐化原理，熟悉拉普拉斯算子的公式和实现，运用拉普拉斯算子对图像进行空间域锐化，并编程实现。

设计型

必做

1

计算机，C++和MATLAB软件

4

频率域低通和高通滤波

1

学会两种简单的频域低通和高通滤波方法。并编程实现傅立叶变换方法；使用布特沃斯和高斯滤波器进行低通滤波；使用布特沃斯和高斯滤波器进行高通滤波

验证型

必做

1

计算机，C++和MATLAB软件

5

数字图像复原

1

了解Fourier变换、反变换的算法实现，掌握频域逆滤波和维纳滤波图像复原的方法。并编程实现：用Fourier变换算法对图像作二维Fourier变换。用Fourier反变换算法对图像作二维Fourier反变换。

验证型

必做

1

计算机，C++和MATLAB软件

6

人脸皮肤颜色分层

2

理解彩色图像的颜色分层原理和方法，利用颜色分层方法对人脸身份证彩色图像进行分层处理，实现对身份证标准图像的背景分离和皮肤区域提取。

设计型

必做

1

计算机，C++和MATLAB软件

7

课程论文（大作业）

20

课程论文（大作业，20学时）：公布一系列大作业题目供学生选择，3人一组完成选定的大作业，组内学生各有分工，提高学生的动手能力和实践能力，使学生能够学以致用，具备进行数字图像处理的算法设计和系统设计能力，学习如何撰写论文，为进一步的研究打下扎实的基础。

探索型

必做

3

计算机，C++和MATLAB软件

Course Code

045101212

Course Title

Digital Image Processing

Course Category

Specialty- related Courses

Course Nature

Elective Course

Class Hours

32

Credits

2

Semester

5 and 7

Institute

School of Computer Science and Engineering

Program Oriented

Computer science and technology, information security

Teaching Language

Chinese

Prerequisites

Advanced Language Programming(C++), Data Structure

Student Outcomes

 (Special Training Ability)

1. Research: An ability to conduct investigations on complex network engineering problems based on scientific theories and by adopting scientific methods, including design of experiments, analysis and interpretation of data, and synthesis of information, to obtain effective conclusions.

2. Applying Modern Tools: An ability to develop, select and apply appropriate techniques, resources, and modern engineering and IT tools for complex network engineering problems, including prediction and modeling of complex engineering problems with an understanding of the limitations.

Teaching Objectives

(1)Master the basic concepts, basic principles and implementation methods and practical technology on digital image processing

(2)Understand the basic application of digital image processing and the current domestic and international development direction on digital image processing.

(3)With the initial ability to solve application problems, and have solid theoretical basis in the field of computer vision, pattern recognition.

(4)Have the ability to design a simple image processing system.

Course Description

This course is a professional course for undergraduates majoring in computer science. This course mainly teaches the formation and development of digital image processing, spatial domain image enhancement, frequency domain image enhancement, image restoration, color image processing, image reconstruction and so on. Through the theory and practice of this course, students can master the basic concepts, principles and algorithms of digital image processing, master the basic tools of digital image processing programming, master the digital image processing algorithm design method, understand the development and application of digital image processing As well as the current international and domestic research achievements and its engineering application prospects. For the computer vision, pattern recognition and other fields engaged in research and development to lay a solid theoretical foundation. In addition to requiring students to complete the general experiment, but also need to complete a Course Design.

Instruments and Equipments

Computers, C++ and Matlab Software

Experiment Report

Individual report. One report for each task.

Assessment

Checks on site and marking lab report.

Teaching Materials and Reference Books

[1] R.C.Gonzalez, Yuan Qiuqi（translate）, Digital Image Processing, Second edition, Tsinghua university Press, 2017

[2] Zhang Xingming, Digital Image Processing Experiment Guide,2019.

Prepared by Whom and When

Zhang Xingming，May, 2019

No.

Experiment Item

Class Hours

Content Summary

Category

Requirements

Number of StudentsEach Group

Instruments, Equipments and Software

1

Digital image reading and color, grayscale, contrast conversion

1

Understand the storage format of digital images, and learn to do some simple visual features of the image. Read the image data from the most commonly used ".BMP" image format; have a preliminary understanding of the representation of the digital image (such as RGB, YUV) and the various representations; change the color of the digital image according to the input parameters, Brightness, contrast.

Demonstration

Compulsory

1

Computer, C++ or MATLAB software

2

Digital image grayscale transformation and gamma correction

1

Understand the basic principle of gray image inverse transform and g correction of digital image. Students' own programming to achieve image reversal; logarithm and exponential transformation; segmentation linear transformation and gamma correction image enhancement algorithm.

Demonstration

Compulsory

1

Computer, C++ or MATLAB software

3

Digital image smoothing and sharpening

2

Learn to remove the noise from the image with a filter. And programming to achieve: with the mean filter to remove the noise in the image; with the median filter to remove the noise in the image; and compare the results of the two methods of processing. Understand the spatial domain sharpening principle of the image, familiarize the formula and implementation of the Laplace operator, use the Laplacian operator to sharpen the spatial domain of the image and program it.

Design

Compulsory

2

Computer, C++ or MATLAB software

4

Frequency domain low pass and high pass filtering

1

Learn two simple frequency domain low-pass and high-pass filtering methods. And programming to implement Fourier transform methods; low-pass filtering using Butterworth and Gaussian filters; high-pass filtering using Butterworth and Gaussian filters

Demonstration

Compulsory

1

Computer, C++ or MATLAB software

5

Image Restoration

1

Understand the Fourier transform, inverse transform algorithm implementation, master the frequency domain inverse filter and Wiener filter image restoration method. And the realization of the program: Fourier transform algorithm for the image of the two-dimensional Fourier transform. Fourier inverse transform of image using Fourier inverse transform algorithm.

Demonstration

Compulsory

1

Computer, C++ or MATLAB software

6

Face skin color segmentation

2

Understand the color stratification principle and method, use the color stratification method to stratify the color image of face ID card, and realize the background separation and skin area extraction of the standard image of ID card.

Design

Compulsory

2

Computer, C++ or MATLAB software

7

Course papers (Course project)

20

published a series of course design topics for students to choose, a group of three students completed a course design topics, improve students' practical ability.

Explorations

Compulsory

20

Computer, C++ or MATLAB software