数字图像处理

Digital Image Processing

(本硕贯通课程,MATH6420P)

(Autumn-Winter 2021)

Graphics&Geometric Computing Laboratory

University of Science and Technology of China


Announcements     General     Description      Course Goals     Topics     Texts     Grading  

Syllabus     Assignments     Requirements on Assignments      Professional Conduct     Resources


Announcements


General
 

Time 每周二晚19:00--
Venue 东区5407
Instructor

Prof. Ligang Liu (lgliu@ustc.edu.cn)

Credit 4
Prerequisite 编程(C++或Matlab),微积分,线性代数
   
Webpage http://staff.ustc.edu.cn/~lgliu/Courses/DIP_2021_autumn-winter/default.htm

 


Description

Image processing is a method to convert an image into digital form and perform some operations on it, in order to get an enhanced image or to extract some useful information from it. It is a type of signal dispensation in which input is image, like video frame or photograph and output may be image or characteristics associated with that image. Usually Image Processing system includes treating images as two dimensional signals while applying already set signal processing methods to them. It is among rapidly growing technologies today, with its applications in various aspects of a business. Image Processing forms core research area within engineering and computer science disciplines too.

Prerequisite: C++/Matlab, Calculus, Linear Algebra

 


Course Goals

This course is to teach students to learn image processing including image sampling and quantization, color, point operations, segmentation, morphological image processing, linear image filtering and correlation, image transforms, eigenimages, multiresolution image processing, noise reduction and restoration, feature extraction and recognition tasks, image registration. Emphasis is on the general principles of image processing. Students learn to apply material by implementing and investigating image processing algorithms in Matlab and optionally on Android mobile devices.


Topics
 

This course provides a comprehensive introduction to image processing, including their design, analysis, and implementation. Topics include:
   
·Image data structure
   
·Edge detection
   
·Image restoration
   
·Fourier analysis
   
·Image compression
   
·Multiresolution analysis
   
·Image warping and morphing
   
·Image segmentation
   
·Image understanding
   
·Sparse representation and low rank
   
·Data-driven methods
 


Texts
 

References:

        Other researchers' courses

Readings:
            Various journal papers, conference papers, or WWW materials as appropriate.
 


Grading

Credit toward the semester grade will be allocated to each of the components as indicated in the following table.

Homework 70%
Final Project 30%

Note: No final examination will be held in the end of this course. Programming projects and literature survey report are both required. More information will be provided in the class.


Syllabus

课程PPT可在睿客网下载:课程相关资料下载

 

Low level image processing

01

Introduction

 

02

Image as points

Statistics based processing

03

Manifold learning

04

Image as function

Fitting and warping

05

Gradient based processing

06

Partial differential equations (PDE)

07

Meshes and barycentric coordinates

08

Vectorization

09

Content aware image retargeting

10

Fourier analysis and processing

11

Convolution

12

Filtering

13

Image pyramids

14

Image denoising

15

High dynamic range images

16

Transposed convolution

17

Image deblurring

18

Image restoration

19

Image morphing

20

Image as matrix

Low rank optimization

21

Matrix factorization

22

Image as graph

Graph cut

23

Image as MRF

Texture synthesis

High level image processing

24

Features

Feature descriptors

25

Photography

Camera

26

Depth and defocus

27

Multi-view geometry

28

Panoramas

29

Light field

30

Image based lighting

31

Analysis and understanding

Introduction

32

Image formation

33

Edge detection

34

Segmentation

35

Matting

36

Object recognition

37

Deep learning

38

Deep neural networks

39

Convolution neural networks

40

Semantic segmentation

41

View morphing

42

Image synthesis

43

Fundamentals

Mathematical theory of deep learning

44

Mathematical models of AI

45

Optimization algorithms

46

Internet Imaging

Introduction

47

Super resolution

48

Scene completion

49

Image2GPS

50

Others

51

Video processing

Optical flow

52

Motion magnification

53

Colorization

54

Video texture

55

Video stabilization

56

Photo quality

Assessment

57

Photo composition optimization

58

Aesthetic evaluation

59

Image quality assessment

60

Summary

 

 

Assignments


Requirements on Assignments
 

Requirements

Assignment Submission

What constitutes Creativity ?

Creativity is any substantial improvement beyond the basic solution - it can be applied to any part of the project. For example, the following are relevant in most cases :


Professional Conduct

As a student in our class, you are expected to conduct yourself in a professional manner.

Limited Collaboration Policy. Unless otherwise indicated, any homework assignment given in this class will be an individual assignment. The work you submit is to reflect the knowledge, understanding, and skill that you have attained as an individual. However, the instructor does want to encourage the development of a community of scholars who are actively engaged in discussion of the ideas related to this course. With this in mind, you are allowed to discuss solutions of the homework and programming problems with other students if done so according to the following guidelines:


Resources

C++ coding styles

Computer Program Documentation Standards

Image processing:

Advices on Researches:

Useful coding related sites on the internet:


Send any comments or suggestions to Prof. Ligang Liu (lgliu@ustc.edu.cn)
Copyright © 2021, Ligang Liu