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.

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General Information

Lecturer

• Ronald Lok Ming Lui LUI
  • Office: LSB 207
  • Tel: 3943-7975
  • 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 6:30pm-9pm (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, 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 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, shift-invariant 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

• Coding course material (Feb. 1, 2024)

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 (6:30PM-8:30PM, March 14, 2024, conducted in class)		35%
Final (6:30PM-9:00PM, 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.

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Assessment Policy

Last updated: April 21, 2024 12:04:52