Fall 2026, International Undergraduate Class
Image Processing is a professional elective course for international undergraduate students in computer science and technology, closely related to engineering practice and intelligent information processing applications. The course focuses on core problems in digital image acquisition, representation, enhancement, restoration, segmentation, description, recognition, and compression, and systematically introduces the fundamental concepts, theories, common techniques, and representative methods of digital image processing. Course topics include an overview of digital image processing, fundamentals of digital images, gray-level transformations and histogram processing, spatial filtering, frequency-domain filtering, image restoration, morphological image processing, image segmentation, color image processing, image representation and description, object recognition, and image compression. Through this course, students will understand and master the basic theories and methods of image processing, learn to select appropriate processing workflows and algorithms for engineering problems such as image quality improvement, feature extraction, object analysis, and image storage and transmission, understand the applicability, parameter effects, and limitations of different methods, and build a solid foundation for further study and practice in image information processing, computer vision, and intelligent systems.
There is no required text for this course. A reference textbook is “Digital Image Processing” by Rafael C. Gonzalez and Richard E. Woods. Lecture slides, notes, and other materials will be posted periodically on this page.
There will be two written assignments and a final examination. We try very hard to make questions unambiguous, but some ambiguities may remain. Ask if confused or state your assumptions explicitly. Reasonable assumptions will be accepted in case of ambiguous questions.
Unless otherwise specified, the lecures are held twice a week: 14:00–15:45 on Tuesdays and 10:30–12:15 on Thursdays, both in Room H403. There will be a break in the middle of each lecture.
| Date | Topics | Textbook | Material | |
|---|---|---|---|---|
| Week #1 | TBD | Introduction to Digital Image Processing - Basic concepts and application areas - Image processing pipeline and system overview - Course introduction and objectives Digital Image Fundamentals - Visual perception system - Light and electromagnetic spectrum - Image acquisition, sampling, and quantization - Pixel relationships |
Chapters 1, 2 | [slides] [notes] |
| Week #1 | TBD | Image Enhancement: Point Processing - Gray-level transformation basics - Negative, log, power-law, piecewise transforms - Histogram equalization and specification |
Chapter 3.1-3.3 | [slides] [notes] |
| Week #2 | TBD | Image Enhancement: Spatial Filtering - Convolution and correlation fundamentals - Smoothing and order-statistic filters - Sharpening and hybrid enhancement methods |
Chapter 3.4-3.7 | [slides] [notes] |
| Week #2 | TBD | Image Enhancement: Frequency Domain Filtering - Fourier transform and 2D DFT basics - Frequency domain filtering framework - Low-pass, high-pass, selective filtering |
Chapter 4 | [slides] [notes] |
| Week #3 | TBD | Image Restoration - Degradation and noise models - Denoising and periodic noise removal - Inverse filtering and Wiener filtering |
Chapter 5 | [slides] [notes] |
| Assignment | TBD | Assignment 1 Release | [questions] [solutions] | |
| Week #3 | TBD | Morphological Image Processing I - Set theory and structuring elements - Erosion and dilation operations - Opening, closing, hit-or-miss transform |
Chapter 9.1-9.4 | [slides] [notes] |
| Week #4 | TBD | Morphological Image Processing II - Boundary extraction and region filling - Connected components and skeletonization - Gray-scale morphology basics |
Chapter 9.5-9.7 | [slides] [notes] |
| Week #4 | TBD | Image Segmentation I - Fundamentals of image segmentation - Edge detection: points, lines, boundaries - Edge linking and detection models |
Chapter 10.1-10.2 | [slides] [notes] |
| Deadline | TBD | Assignment 1 Due | ||
| Week #5 | TBD | Image Segmentation II - Thresholding methods - Otsu and adaptive thresholding - Region growing and watershed segmentation |
Chapter 10.3-10.6 | [slides] [notes] |
| Week #5 | TBD | Color Image Processing I - Color perception and models - RGB, CMY/CMYK, HSI systems - Pseudocolor and gray-to-color transformation |
Chapter 6.1-6.4 | [slides] [notes] |
| Week #6 | TBD | Color Image Processing II - Color transformations and histogram processing - Color smoothing and sharpening - Color-based segmentation |
Chapter 6.5-6.9 | [slides] [notes] |
| Assignment | TBD | Assignment 2 Release | [questions] [solutions] | |
| Week #6 | TBD | Image Representation & Description I - Boundary representation methods - Chain codes and polygon approximation - Boundary and skeleton representation |
Chapter 11.1 | [slides] [notes] |
| Week #7 | TBD | Image Representation & Description II - Fourier descriptors and moments - Region descriptors and texture features - Invariant representations and PCA |
Chapter 11.2-11.5 | [slides] [notes] |
| Week #7 | TBD | Object Recognition - Pattern and pattern classes - Decision-theoretic recognition - Statistical classifiers and neural methods |
Chapter 12 | [slides] [notes] |
| Deadline | TBD | Assignment 2 Due | ||
| Week #8 | TBD | Image Compression - Compression fundamentals and redundancy - Huffman, arithmetic, LZW coding - Run-length and predictive coding |
Chapter 8 | [slides] [notes] |
| Week #8 | TBD | Course Review - Review of key concepts across the course - Final summary and consolidation |
Review 1-12 | |
| TBD | Final Examination | [sample] |
Assignments are an important part of this course. They are designed to help you review the core concepts introduced in lectures, practice mathematical analysis, and develop the ability to design workflows for practical scenarios.
The assignments are based on the lecture topics and course materials. They ask you to explain concepts, perform small calculations, compare methods, and design reasonable processing pipelines for real problems. Each assignment contains three types of questions:
Conceptual Explanation and Comparative Analysis
These questions ask you to compare image-processing concepts or methods in practical contexts, such as human viewing versus machine recognition, enhancement versus restoration, or global versus local segmentation decisions.
Mathematical Computation and Formula Application
These questions test your ability to use formulas and perform quantitative reasoning, such as computing storage size, pixel distances, histogram mappings, morphological operations, thresholding criteria, or color-space values.
Algorithmic Logic and Process Design
These questions ask you to describe the steps of an image-processing workflow and explain the purpose of each stage. You should be able to connect acquisition, enhancement, filtering, morphology, segmentation, and color processing into coherent pipelines.
Late submissions will receive a reduced score based on the grade the work would have earned if submitted on time: 80% for submissions up to 24 hours late, 60% up to 2 days late, 40% up to 3 days late, 20% up to 4 days late, and 0% after 4 days late. A partial day counts as a full late day; for example, work worth 80/100 submitted 36 hours late would receive 64 points. Please start early and allow enough time to prepare your submission.
Students are encouraged to discuss lecture concepts, general problem-solving strategies, and high-level ideas with classmates, but each student must write and submit their own work independently. You may not copy another student’s solution, share completed answers, write solutions jointly, or submit work produced by another person or system as your own. Any external help should be used only to support your understanding, and your final submission must reflect your own reasoning.
You may use generative AI tools to support your learning, for example to clarify concepts, check grammar, or ask for hints about general problem-solving strategies. However, the submitted work must represent your own understanding. You should not submit AI-generated answers directly, and you are responsible for verifying all formulas, calculations, explanations, and conclusions. If you use generative AI in preparing your assignment, please acknowledge it briefly in your submission and describe how it was used.