Semester: 4

Credit: 2

Program: Computer science,Electronics

Course Module: Optional

Responsible: Jianrong Wang

E-mail: wjr@tju.edu.cn

Department:  Tianjin International Engineering Institute

Time Allocation (1 credit hour = 45 minutes)

Course Description

The course is optional designed for Engineering Mater of Computer Science in International Engineering Institute. This course will systematically describe how to process the images in a digital way. This course will introduce the basic concepts of image processing, the basic knowledge of image and the commonly used methods of image processing. Emphasis will be given to the key points, such as image enhancement and image segmentation, etc. Besides, some experimental projects will be combined to cultivate students’ innovation ability. Through learning of this course, it will make students understand the characteristics of digital image processing, master the basic methods of image processing providing the necessary knowledge and training skills for those students who want to be engaged in the digital image processing in the future.

Prerequisite

• Signals and Systems: master the methods of signal and systems.

• Digital Signal Processing: understand the basic theories and methods.

• Understand the basic knowledge of Information theory and Coding: understand the general rule of information extraction, transmission and processing.

Course Objectives

This course discusses the basic concepts and methods of digital image processing to help students process the images better and enhance their professional skills. After this course, students should be able to:

• Master the basic principle of image processing,

• Master the commonly used methods and its implementation algorithms of the digital image processing, and to

• Know about the hardware environment image processing systems involves.

Course Syllabus

• Summary of digital image processing: digital image, digital image processing system.

• Image and visual basic: digital image processing foundation, visual and brightness, sampling and quantization.

• Image transformation: image transformation, discrete Fourier transform and its properties, discrete cosine transform.

• Image enhancement: image enhancement, spatial domain transform, colour enhance.

• Image restoration and reconstruction: image restoration and reconstruction, image degradation, image reconstruction principle.

• Image coding: image compression, data redundancy, simple encoding method.

• Image segmentation: image segmentation, edge detection method, image threshold segmentation method.

Textbooks & References

• Rafael C. Gonzalez and Tichard E. Woods. Digital Image Processing (3nd ed). Prentice Hall, 2007.

• Kenneth R. Castleman. Digital Image Processing. Prentice Hall, 1995.

• S. K. Ghosh. Digital Image Processing. Alpha Science International Ltd, 2012.

• Bernd Jahne. Digital Image Processing: Concepts, Algorithms, and Scientific Applications. Springer-Verlag Berlin and Heidelberg Gmbh& Co. K, 1997.

Capability Tasks

CT1: To understand the basic knowledge of digital image processing, and can use them to analyze images.

CT2: To understand the basic concepts of digital image processing.

CT3: To master the methods and tools of digital image processing so as to analyze the image.

CS1: To master the basic theories of digital Image processing, and to understand its development status and trends.

CS2: To master the core knowledge and tools of digital image processing, and to apply them to engineering technology.

Achievements

• To understand the concepts of image and the digital image. - Level: N

• To understand digital image and digital image processing system. - Level: N

• To understand the connection between the pixels. - Level: N

• To understand the used mathematical tools. - Level: N

• To understand the image transformation. - Level: N

• To learn to use the discrete Fourier transform and its properties. - Level: A

• To understand the image restoration and reconstruction. - Level: N

• To understand the image degradation model and the constrained and unconstrained recovery. - Level: N

• To understand the image compression. - Level: N

• To learn to use image segmentation. - Level: A

• To learn to use the edge detection method and the image threshold segmentation method. - Level: A

Students: Computer science,Year 2