MATH3360 - Mathematical Imaging - 2025/26
Announcement
- Homework 3 has been posted. It will be due on October 21 before 11:59PM. Please submit your homework through the Blackboard system.
- Homework 2 has been posted. It will be due on October 10 before 11:59PM. Please submit your homework through the Blackboard system.
- Homework 1 has been posted. It will be due on September 26 before 11:59PM. Please submit your homework through the Blackboard system.
- There will be no tutorial in the first week of class.
General Information
Lecturer
-
Ronald Lok Ming LUI
- Office: LSB 207
- Tel: 39437975
- Email:
Teaching Assistant
-
Ka Ho LAI
- Office: LSB 222B
- Email:
- Office Hours: By Appointment
Time and Venue
- Lecture: Tue 4:30PM-6:00PM (LSB LT3); Thurs 1:30PM-2:15PM (Leung Kau Kui Bldg 101)
- Tutorial: Thurs 12:30PM to 1:15PM (Leung Kau Kui Bldg 101)
Course Description
This course gives an introduction on mathematical models and techniques for various image processing tasks. Our focus will be on the mathematical aspects of different imaging problems. A wide array of topics will be covered, including image restoration (denoising, deblurring), image segmentation, image compression, image registration, deep-learning based imaging methods and so on.
Lecture Notes
- Chapter 1: Basic concepts in Digital Image Processing
- Chapter 2: Image decomposition
- Chapter 3: Image Enhancement in the Frequency Domain
- Chapter 4: Image Enhancement in the Spatial Domain
- Chapter5: Image Segmentation
Class Notes
- Lecture 1 (Class Note): Introduction to image processing, image transformation
- Lecture 1 (PowerPoint): Introduction to Image Processing
- Lecture 2 (Class Note): Point spread function, separable image transformation, discrete convolution
- Lecture 3 (Class Note): Discrete convolution and image decomposition
- Lecture 3 (PowerPoint): Examples of discrete convolution for image processing
- Lecture 4 (Class Note): Transformation matrix and Image decomposition by separable image transformation
- Lecture 5 (Class Note): Image decomposition by SVD
- Lecture 5 (PowerPoint): Examples of image decomposition by SVD
- Lecture 6 (Class Note): Discrete (Haar) Wavelet Transform for imaging
- Lecture 6 (PowerPoint): Examples of discrete Haar Wavelet transform for imaging
- Lecture 7 (Class Note): Introduction to Discrete Fourier Transform (DFT)
- Lecture 7 (PowerPoint): Examples of image decomposition by DFT
- Lecture 8 (Class Note): Properties of Discrete Fourier Transform for imaging
- Lecture 8 (PowerPoint): Examples of DFT image decomposition and JPEG
- Lecture 9 (Class Note): More properties about DFT for imaging
- Lecture 10 (Class Note): Low/High-pass filtering
- Lecture 11 (Class Note): Examples of low/high pass filtering, introduction to image deblurring
- Lecture 11 (PowerPoint): Examples of low/high pass filtering
- Lecture 12 (Class Note): Image deblurring: Speeding problem and direct inverse filtering
- Lecture 13 (Class Note): Image deblurring: Butterworth inverse filerting & Weiner filtering & Constrained Least Square filtering
- Lecture 13 (PowerPoint): Image deblurring in the frequency domain: Direct inverse filtering, Butterworth inverse filtering, Wiener filtering
- Lecture 14 (Class Note): Image deblurring by Constrained Least Square filtering
- Lecture 14 (PowerPoint): Examples of Constrained Least Square filtering
- Lecture 15 (Class Note): More about Constrained Least Square filtering & Image Shapening
- Lecture 15 (PowerPoint): Examples of Laplacian masking
- Lecture 16 (Class Note): Unsharp masking, linear/non-linear filtering
- Lecture 16 (PowerPoint): Examples of unsharp masking, linear/non-linear filtering
- Lecture 17 (Class Note): Imaging by PDE and Minimization, TV denoising model
- Lecture 17 (PowerPoint): Examples of image denoising using Anisotropic Diffusion
- Lecture 18 (Class Note): TV denoising model
- Lecture 19 (Class Note): More about TV denoising and Active Contour Segmentation model (Incomplete, more will be conveyed in the class)
Tutorial Notes
- OneDrive to Past Tutorials (for reference only, beware of the difference in syllabus)
- Tutorial 1 Note
- Tutorial 1 Code
- Extra Coding Practice for Convolution (Not Compulsory)
- A Graphical Explanation to Extra Coding Practice (Not Compulsory)
- Tutorial 2 Note
- Tutorial 2 Code
- Tutorial 3 Note
- Tutorial 3 Code
- Tutorial 4 Note
- Tutorial 4 Code
- Tutorial 5 Note
- Tutorial 5 Code
- Tutorial 6 Note
- Tutorial 7 Note (Amended on 03/11)
- Tutorial 7 Code
- Tutorial 8 Note
- Tutorial 8 Code
Assignments
- Assignment 1 (Due on September 26 before 1159PM) (Amended on 15/09)
- Assignment 2 (Due on October 10 before 1159PM)
- Assignment 2 Coding Exercise
- Assignment 3 (Due on October 21 before 1159PM)
- Assignment 4 (Due on November 14 before 1159PM) (Added hint for Q3 on 06/11))
- Assignment 4 Coding Exercise
Quizzes and Exams
- Midterm Practice (Added Q19)
- Midterm Practice Solution (Added Q19)
- Midterm
- Midterm Solution
- Final Practice
Solutions
- Solution for Assignment 1
- Solution for Assignment 2
- Solution for Assignment 2 Coding Exercise
- Solution for Assignment 3
- Solution for Assignment 4
- Solution for Assignment 4 Coding Exercise
Assessment Scheme
| Homework | 15% | |
| Midterm (October 23, 12:30PM to 2:15PM, in class) | 35% | |
| Final (TBA) | 50% |
Honesty in Academic Work
The Chinese University of Hong Kong places very high importance on honesty in academic work submitted by students, and adopts a policy of zero tolerance on cheating and plagiarism. Any related offence will lead to disciplinary action including termination of studies at the University. Although cases of cheating or plagiarism are rare at the University, everyone should make himself / herself familiar with the content of the following website:
http://www.cuhk.edu.hk/policy/academichonesty/and thereby help avoid any practice that would not be acceptable.
Assessment Policy Last updated: November 18, 2025 14:01:28