ELEC_ENG 420: Digital Image Processing

Quarter Offered

None : TuTh 1:00-2:20 ; Cossairt & Willomitzer


ELEC_ENG 359 or equivalent.


Course Goals

To study the application of digital signal processing to problems in image processing. Topics covered will range from the fundamentals of 2-D signals and systems, to image enhancement, restoration and segmentation.

Course Description

The field of Image Processing is concerned with the study of computational approaches for the analysis, storage and interpretation of digital content. In modern times, the sphere of influence of Image Processing has expanded to include an assortment of fields ranging from medical diagnostics to autonomous navigation. This course provides introduces students to basic concepts and techniques in digital image processing. Topics covered will include characterization and representation of digital images, image enhancement, image restoration, image analysis, and image segmentation.


Desired: Introduction to Signal Processing or equivalent, Introduction to Linear Algebra or equivalent.

Required: Ability to program. The homework assignments and the exams will involve substantial amount of programming in MATLAB.

Textbook (Optional)

R. C. Gonzalez and R. E. Woods, Digital Image Processing, 4th Edition, Pearson, 2018

Time & Place

Tuesdays and Thursdays 1:00pm-2:20pm CT

ECE420 lecture: All lectures will held live on zoom and linked through canvas. Lectures will also be recorded for those who cannot attend during scheduled class times.

Instructors & Office Hours

Oliver Cossairt Office Hours: Thursday 3-5PM - write an email to oliver.cossairt (a) to book a 10min slot.

Florian Willomitzer Office Hours: Thursday 3-5PM - write an email to to book a 10min slot.

Teaching assistants & Office Hours

Jiazhang Wang Mail: JiazhangWang2024 (a)

Office hours: TBD.


  • Grading: Homeworks are worth 80% of the grade, and there will be 5 homework assignments worth 12.5% of your grade each. Assignments will consist of a coding and a technical writeup. Your coding must be correct, and your writeup must be clearly written. The Final is worth 20% of the grade and will be open-book and open-notes, but you may not discuss the test with anyone or consult with others. 100%-95% is an A, 95%-90% is an A-, 89%-85% is B, 84%-80% is B-, 79%-70% is C, 69%-60% is D.

  • When and Where to Submit Assignments: Your matlab code and a PDF writeup report for each assignment must be submitted on Canvas by 11:59pm on the due date.

  • Late Policy: All Homeworks are to be submitted via Canvas by 11:59pm on the due date. Each student will be permitted ONE late submission for partial credit. Two points shall be docked from the submission for each 24-hour period. For instance, if the homework is due Tuesday at 11:59pm and it is submitted Wednesday between 12:00am and 11:59pm, 2 points will be docked. If the assignment is submitted on Thursday between 12:00am and 11:59pm, 4 points will be docked, and so on. Only ONE late assignment per student will be awarded partial credit. Any additional late assignments will receive no credit.

  • Cheating & Academic Dishonesty: Do your own work. This includes free response answers and code. Penalties include failing the class and can be more severe than that. If you have a question about whether something may be considered cheating, ask, prior to submitting your work. We will be checking for code duplication. Academic dishonesty will be dealt with as laid out in the student handbook.

  • Attendance is mandatory but not graded.

  • Announcements and discussions will take place on Canvas.

Lecture Calendar

This is a prediction of what will be covered in each week but the schedule is subject to change as the course progresses.

Week of Lecture of week Topic
1/12 Tue Introduction
1/14 Thu Image Formation
1/19 Tue Mathematical tools for digital image processing
1/21 Thu Histogram Processing
1/26 Tue Spatial Domain Filtering I
1/28 Thu Spatial Domain Filtering II
2/02 Tue Fourier Domain Processing I: 1D Signals
2/04 Thu Fourier Domain Processing II: 2D Signals (Images)
2/09 Tue Image Restoration
2/11 Thu Morphological Procesing
2/16 Tue Edge Processing I: Detection
2/18 Thu Edge Processing II: Hough Transform
2/23 Tue Feature Extraction I: Corner Detection
2/25 Thu Feature Extraction II: SIFT
3/02 Tue Image Segmentation I: KMeans and Mean Shift
3/04 Thu Image Segmentation II: Graph Methods
3/09 Tue Final Review
3/11 Thu Final


See CANVAS for the link to assignments. Homework is due and assigned on the dates below.

Date Assigned Due
1/19 HW 1: Image Processing  
1/28 HW 2: Image Enhancement HW 1: Image Processing
2/09 HW 3: Image Restoration HW 2: Image Enhancement
2/18 HW 4: Edge Processing HW3: Image Restoration
3/02 HW 5: Hough Transform HW 4: Edge Processing
3/11   HW 5: Hough Transform


Course lectures and materials are adapted from the course EE 5374/7374 – Digital Image Processing, offered by Southern Methodist University. Thanks and acknowledgements to Prof. Prasanna Rangarajan for sharing materials from this course.