MMAT5390 - Mathematical Image Processing - 2024/25

Announcement

• Final examination will be held on April 24 from 3:30pm to 6pm at Science Centre L1.
• Assignment 5 has been posted.
• Assignment 4 has been posted.
• Assignment 3 has been posted. It will be due on March 12 before 1159PM. Please submit the HW through the Blackboard System.
• The due date of Assignment 2 has been postponed to Feb 28 before 11:59PM.
• Assignment 2 has been posted. It will be due on Feb 24 before 1159PM. Please submit the HW through the Blackboard System.
• Assignment 1 has been posted. It will be due on Feb 5 before 1159PM. Please submit the HW through the Blackboard System.

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General Information

Lecturer

• Ronald Lok Ming Lui LUI
  • Office: LSB 207
  • Tel: 3943-7975
  • Email:

Teaching Assistant

• Yulin Wei
  • Office: LSB 222B
  • Tel: 3943 7963
  • Email:
• Hei Tung Tsang
  • Office: SC 333B
  • Email:

Time and Venue

• Lecture: Th 3:30PM - 6:15PM, Science Centre L1

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, multi-scale image analysis 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 Enchancement in the Spatial Domain

Class Notes

• Lecture 1 (Class Note): Image transformation and point spread function
• Lecture 1 (Powerpoint): Basic background about image processing
• Lecture 2 (Class Note): Image convolution
• Lecture 2 (PowerPoint): Examples of image convolution
• Lecture 3 (Class Note): Image similarity, Transformation matrix, Image decomposition and SVD
• Lecture 4 (Class note): More about SVD & Haar Wavelet transform
• Lecture 4 (Powerpoint): Examples of image decomposition by SVD and Haar transformation
• Lecture 5 (Class note): Discrete Fourier Transform for image processing
• Lecture 5 (PowerPoint): Image decomposition by DFT
• Lecture 6 (Class note): More about DFT for image processing
• Lecture 6 (PowerPoint): Mathematics of JPEG
• Lecture 7 (Class note): Image enhancement in the frequency domain
• Lecture 7 (PowerPoint): Examples of low-pass & high-pass filtering
• Lecture 8 (Class note): High pass filtering and Introduction to image deblurring
• Lecture 8 (PowerPoint): Examples of high-pass filter
• Lecture 9 (Class note): More about image deblurring: Modified Inverse Filtering & Wiener Filtering
• Lecture 9 (PowerPoint): Examples of direct inverse filtering, modified inverse filtering and Wiener’s filtering
• Lecture 10 (Class note): More about image deblurring: Constrained Least Square Filtering
• Lecture 10 (PowerPoint): Examples of Constrained Least Square Filtering
• Lecture 11 (Class note): Image sharpening, Linear/non-linear filtering, Image Processing by PDEs
• Lecture 11 (PowerPoint): Examples of image sharpening, linear/non-linear filtering
• Lecture 12 (Class note): Image processing by PDEs and Energy Minimization
• Lecture 12 (PowerPoint): Examples of isotopic and anisotropic diffusion for image denoising
• Lecture 13 (Class note) TV denoising
• Lecture 13 (PowerPoint): Examples of TV denoising

Tutorial Notes

• Programming Tutorial (13/02/2025)

Assignments

• Assignment 1 (Due: 23:59 Wednesday, Feb 5, 2025)
• Assignment 2_new (Due: 23:59 Monday Feb 24, 2025, postponed to Feb 28)
• Assignment 3 (Due: 23:59 Wednesday, March 12, 2025)
• Practice midterm (No need to submit)
• Assignment 4 (Due: 23:59 Wednesday, April 2, 2025)
• Assignment 5 (Due: 23:59 Tuesday, April 22, 2025)
• Practice Final (No need to submit)

Solutions

• Solution 1 (prepared by TA, for reference only)
• Solution 2 (prepared by TA, for reference only)
• Solution of practice midterm (prepared by TA, for reference only)
• Solution 3 (prepared by TA, for reference only)
• Solution 4 (prepared by TA, for reference only)
• Solution of midterm (prepared by TA, for reference only)
• Solution of practice final (prepared by TA, for reference only)
• Solution 5 (prepared by TA, for reference only)

Assessment Scheme

Homework		15%
Midterm (3:30PM-5:30PM, March 13, 2025, conducted in class)		35%
Final (3:30PM-5:30PM, April 24, 2025, conducted in class)		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.

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Assessment Policy

Last updated: April 23, 2025 07:10:39