数字图像处理

课程简介 Course Introduction

Digital Image Processing (DIP) is a required course for those students in the Digital Media Technology class with the major of Software Engineering. It can also be a selective course for other undergraduate or graduate students of Software Engineering or other related Majors. The course is about the fundamental concept, basic skills, practical technologies and some typical applications in DIP. The content includes: Basic Elements of Visual Perception; Image Sensing and Acquisition; Basic image features and transformations; Image enhancement in both spatial and frequency domains; Image restoration; Color fundamentals; Color image processing, etc.Upon studying of this course, the outcomes would be: (1) Students will understand the fundamental concepts, main technologies, and some key applications of DIP, as well as the relationships of these issues. (2) Students will be able to use at least Matlab to manipulate images, and they will have the basic skills of image analysis and get basic sense of how image processing solutions are prototyped in software. (3) Enrich students’ knowledge and enhance their abilities in DIP in order for them to have further study for related courses and research areas. Encourage the student to have curiosity and courage for exploration and innovation.

教学大纲 Teaching Syllabus

Introduction and Digital Image Fundamentals (6 hr): Introduce several key applications of DIP; Basic Elements of Visual Perception; Image Sensing and Acquisition; Image Sampling and Quantization; Some Basic Relationship between Pixels; Linear and Non-linear Operations. (P
Image Enhancement in the Spatial Domain (6 hr): some basic gray level transformations; Histogram processing; Enhancement using Arithmetic/Logic operations; Basics of spatial filters; smoothing filters; sharpening filters;
Image Enhancement in the Frequency Domain (9 hr): introduction to Fourier transform and the frequency domain; Smoothing and sharpening frequency-domain filters; Homomorphic filtering.
Midterm Examination
Image Restoration (6 hr): a model of the image degradation/restoration process; Noise models; restoration in the presence of noise only-spatial filtering; periodic noise reduction by frequency domain filtering; linear, position-invariant degradation (brief); estimating the Degradation function (brief); inverse filtering.
Color Image Processing (6 hr): color fundamentals; color models; pseudo-color image processing; basics of full-color image processing; color transformation; smoothing and sharpening; color segmentation; noise in color images; color image compression.
Image compression (3 hr, optional): Brief introduction to the main concept and frame work of image/video compression.
Morphological image processing (6 hr): introduction to the basic concept of morphological image processing; dilation and erosion; opening and closing; the Hit-or-Miss transformation; some basic morphological algorithm.
Image Segmentation (6 hr, optional): introduction to the basic concept of morphological image processing; dilation and erosion; opening and closing; the Hit-or-Miss transformation; some basic morphological algorithm.
Representation and Description (6 hr, optional)
Object Recognition (6 hr, optional)

Totally 54 hours.