本课程适合想学习各类(光电、计算机、医学等)图像处理基础、对指纹人脸识别等机器视觉感兴趣的零基础学生。
这门课主要介绍数字图像的基础知识和处理方法,基础理论讲授与生活相关案例分析结合,教师演示与简单编程(Matlab或Python)体验结合。
通过本课程的学习,可以明白日常图像背后的原理,提升你的解决问题能力、编程能力和思维创新能力。
此外,本课程以双语形式讲授,可以帮助你具有国际的视野和包容的胸怀。
This course suits those would like to learn the basics of image processing for optoelectronic, computer, medical images etc., and those interested in computer vision like fingerprint detection, humain face recognition etc without any background of digital image processing.
This course mainly introduces the basic knowledge and processing methodologies of digital image. It covers fundamental theories combining with related cases in daily life. The lecturer will demonstrate typical processing and you may also experience simple programming (Matlab or Python) to process images yourself.
After you complete the study, you shall be able to understand basic principles behind images in daily life. Also, the problem solving ability, programming skills, as well as creative mind will be improved.
In the meanwhile, you may know more about China and thinking of Chinese people.
技能类目标(Psychomotor objectives):
A. 学生通过本课程学习,可以通过直方图和傅里叶频域评价图像质量,并运用直方图均衡和合适的(空间或频域)滤波器对图像进行增强。
After studied this course, the students can evaluate the image quality according to the histogram and Fourier frequency domain, and do image enhancements with histogram equalization and proper (spatial or frequency) filters.
B. 学生通过本课程学习,能剖析图像存在的噪声类型和设计相应的降噪方法。
After studied this course, the students can analyze the noise type of the existing image and design noise reduction approaches.
C. 学生通过本课程学习,可以独立运用Matlab/Python图像处理工具,对日常图片进行综合图像增强和降噪处理。
After studied this course, the students can independently use the image processing tools like Matlab/Python to do comprehensive image enhancement and noise reduction for daily photos.
情感类目标(Affective objectives):
A. 学生在本课程的形成性评价体系指引下可以养成踏实、独立学习的态度。
The students can develop earnest and independent learning attitudes under the supervision of the formative evaluation system.
B. 学生通过本课程交互式的学习体验,可以培养较好人际交往能力、团队合作能力。
The students can develop good interpersonal skills and team work by the interactive learning experience.
C. 中外学生通过课程可以经常交流,促进彼此了解和互相尊重,营造和谐的国际多元校园氛围。
The oversea students and local students can communicate with each other frequently during this course, which enhances the mutual understanding and mutual respect as well as creates a harmonious international diversified campus atmosphere.
认知类目标(Cognitive objectives):
A. 学生通过本课程学习,可以列举数字图像处理的基本步骤,解释数字图像获取过程和相关基本概念。
After studied this course, the students can list the fundamental steps in digital image processing (DIP) and explain the acquisition of digital image as well as related elementary concepts.
B. 学生通过本课程学习,能够区分基本灰度变换函数和典型空间滤波器功能。
After studied this course, the students shall be able to differentiate the features of basic intensity transformation functions and typical spatial filters.
C. 学生通过本课程学习,可以初步理解图像处理中(傅里叶)频域与空间的关系和各自的特点,并能熟练区分三类典型频域滤波器的特性以及归纳对应空间滤波器的异同。
After studied this course, the students shall have a basic concept on the relationship between (Fourier) frequency domain and spatial domain in DIP, distinguish the features of three typical frequency domain filters, and sum up the similarity as well as the difference from the corresponding spatial filters.
D. 学生通过本课程学习,能解释图像退化的模型,并独立运用图像退化模型建构基本的图像复原过程。
After studied this course, the students can explain the image degradation model, and independently construct basic image restoration procedure with the image degradation model.
E. 学生通过本课程学习,可以总结常见的彩色模型的特点,列举伪彩色图像处理的应用,对彩色图像做简单彩色图像变换及理解基于彩色的图像分割基本原理。
After studied this course, the students shall summarize the features of common color models, illustrate the applications of pseudocolor image processing, do simple color transformations to color images, and understand the basic principles of image segmentation based on color.
本课程采用百分制计算,60~89分为合格,90分以上为优秀。
评分标准如下:
线上视频学习完成度 20%
作业 10%
问题讨论 10%
线上单元测试 10%
期末课程项目 50%
This course is graded with 100-point system, Pass score: 60~89, Excellent score: 90 and above.
Grading policy:
Completeness of online video watching 20%
Assignments 10%
Problem discussion 10%
Online unit tests 10%
Final course project 50%
学生具有基本阵列或矩阵概念可以较容易理解图像和图像处理,同时具有Matlab或Python初步编程基础来体验各类图像处理
Preliminary knowledge
The students shall have a basic concept of array or matrix to understand image and image processing easily. Also, basic Matlab or Python programming skill is needed to experience various of image processing.
1. 冈萨雷斯、伍兹,数字图像处理(第三版)(英文版),电子工业出版社,2017 (ISBN: 9787121305405)
2. 冈萨雷斯、伍兹著;阮秋琦等译,数字图像处理(第三版),电子工业出版社,2017 (ISBN: 9787121313837)
3. Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing (4th ed., Global Edition), Pearson Education Limited, 2018 (ISBN: 9781292223049)
4. 冈萨雷斯(R. C. Gonzalez)、伍兹(R. E. Woods)、埃丁斯(S. L. Eddins)著,阮秋琦注释,Digital Image Processing Using MATLAB (2nd ed.),电子工业出版社,2013(ISBN: 9787121195440)
5. Scikit-image Processing for Python: https://scikit-image.org/
6. 岳亚伟主编,数字图像处理与Python实现,人民邮电出版社,2020(ISBN: 9787115527912)
7. 刘衍琦、詹福宇、王德建主编,计算机视觉与深度学习实战:以MATLAB、Python为工具,电子工业出版社,2019(ISBN: 9787121374838)
线上学习交流反馈可以加入钉钉群(群号:31009491)
You can feedback and communicate via joining the Dingtalk group (No.: 31009491)