MMAT5390  Mathematical Image Processing  2023/24
Announcement
 Since the materials necessary for solving Q4 of HW4 could not be covered in the lecture on March 28th, Q4 of HW4 has been updated. Please download the updated version.
 The final exam will be held on April 25th, 2024 from 6:30PM to 9:00PM. It will be conducted in class.
 The due date for Assignment 2 has been postponed to Feb 28 before 1159PM.
 Assignment 2 has been posted. It will be due on Feb 26 before 1159PM.
 Assignment 1 has been posted. It will be due on Feb 5 before 1159PM. Please submit the HW through the Blackboard System.
General Information
Lecturer

Ronald Lok Ming Lui LUI
 Office: LSB 207
 Tel: 39437975
 Email:
Teaching Assistant

Chen Qiguang
 Office: LSB 222B
 Tel: 3943 7963
 Email:

Lin Chenran
 Office: LSB 222A
 Tel: 39433575
 Email:
Time and Venue
 Lecture: Thursday 6:30pm9pm (YIA LT7)
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, multiscale 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 Enhancement 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): Convolution, shiftinvariant and similarity between images
 Lecture 2 (Powerpoint): Examples of convolution on real images
 Lecture 3: (Class note): More about linear image transformation, Image decomposition & SVD for imaging
 Lecture 4: (Class note): More about SVD, Haar Wavelet transform
 Lecture 4: (Powerpoint): Examples of image decomposition by SVD
 Lecture 5: (Class note): Haar transform and DFT for image decomposition
 Lecture 5 (Powerpoint): Image decomposition by Haar Transform and DFT
 Lecture 6: (Class note): Properties of DFT, Mathematics of JPEG
 Lecture 6: (Powerpoint): Mathematics of JPEG: Examples
 Lecture 7: (Class note): More about the properties of DFT and Ideal Low Pass filtering
 Lecture 7: (Powerpoint): Low/High Pass Filtering: Examples
 Lecture 8: (Class note): Low/High Pass Filtering, Mathematical formulation of Image Blur
 Lecture 8: (Powerpoint): Low Pass Filtering & High Pass Filtering
 Lecture 9: (Class note): Image deblurring models
 Lecture 9: (Powerpoint): Examples of image deblurring models
 Lecture 10: (Class note): Image deblurring model: Constrained least square filtering, Image sharpening (incomplete)
 Lecture 10: (Powerpoint): Image deblurring in the frequency domain: Constrained Least Square Filtering
 Lecture 11: (Class note): More about constrained least square filtering, image filtering
 Lecture 11: (Powerpoint): Examples of image sharpening & image filtering
 Lecture 12: (Class note): Imaging by energy minimization, TV image denoising model
 Lecture 12: (Powerpoint): Examples of TV denoising
Tutorial Notes
Assignments
 Assignment 1 (Updated on Q2b)(Due on Feb 5 before 11:59PM. Please submit the solution via the Blackboard system)
 Assignment 2 (Due on February 28 before 1159PM)(Extended)
 Practice Midterm (No need to submit)
 Assignment 3 (Extended, due on March 15 (Friday) before 11:59PM)(Fixed Q1)
 Assignment 4 (Due on April 10 before 11:59PM) (Updated)
 Assignment 4 code (Updated)
 Assignment 5 (Due on April 24 before 11:59PM) (Updated)
 Practice final (Revised on 21/04) (No need to submit)
Solutions
 Solution for assignment 1 (Prepared by TA, for reference only.)
 Solution to practice midterm (Revised on 10/03) (Prepared by TA, for reference only.)
 Solution to assignment 2 (Prepared by TA, for reference only.)
 Solution to Q6 (coding) in assignment 2
 Solution to assignment 3 (Prepared by TA, for reference only.)
 Solution to final practice (Revised on 21/04) (Prepared by TA, for reference only.)
Assessment Scheme
Homework  15%  
Midterm (6:30PM8:30PM, March 14, 2024, conducted in class)  35%  
Final (6:30PM9:00PM, April 25, 2024, 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.
Assessment Policy Last updated: April 21, 2024 12:04:52