ECE558 — Digital Imaging, Spring 2023

Class Time and Instruction Information

Tuesday and Thursday, 12:30–2:00pm

Location: ECEB 2013

The lectures will be recorded and posted under the weekly schedule below.


Teaching assistant

The Gradescope code is K37K43


Course description

The course begins with introducing multidimensional signal theory, which constitutes a mathematical framework to study digital imaging systems. We revisit familiar concepts such as Fourier transform, convolution, sampling and interpolation in higher dimensions. Then we introduce image reconstruction, forward models of image formation, and related concepts of well-posed and ill-posed inverse problems, conditioning and stability. Classical regularization techniques and statistical methods for the solution of inverse imaging problems are introduced, followed by more recent sparsity based methods, and machine learning techniques in computational imaging. In the second half of the course, we study various imaging modalities including optical and diffraction imaging, tomography, radar and lidar, aperture synthesis and interferometry, and phase retrieval.


ECE 310: Digital Signal Processing (or equivalent), and ECE 313: Probability with Engineering Applications (or equivalent)


Course Activities


There will be weekly problem sets assigned up to the week of mid-semester exam (prior to transition to the final project); they include both standard and computational problems. Solutions will be posted on the course website. 


There will be one mid-semester exam scheduled for the week prior to spring break.

Journal Review

There will be one journal article to be chosen and reviewed by each student from a list of relevant research papers which will be posted by mid semester. A four to six page report demonstrating the understanding of the topic will be expected. The article will serve as the starting point for the formation of the final project.

Final Project

There will be a final project consisting of an oral presentation and a written report on a topic of student's choosing related to this course. A list of suggested project topics will be provided. A ten minute oral presentation, a six to ten page report, and a software demo is expected.


All submissions will happen over Gradescope.


Recommended Texts

Week by week

 Time           Topic                                                       Lecture Material   Additional material Reading Assignments

Week 1:

1/16 - 1/20

Overview & Introduction to

Multidimensional Fourier Transform

Lecture 2 Notes

  Blahut 1.1–1.5, 3.1

Homework 1

Homework 1 Solutions

Week 2:

1/23 - 1/27

Circularly symmetric functions, Resolution,

Projection Slice Theorem

Lecture 3 Notes

  Blahut 3.2–3.9

Homework 2

Week 3:

1/30 - 2/3

Sampling, Linear operators, Convolution, DFT


  Blahut 3.2–3.9


Week 4:

2/6 - 2/10

Introduction to Inverse Problems

Tomography application



Week 5:

2/13 - 2/17

Discretization of Inverse Problems, SVD,

Transform domain filtering, tomography application



Week 6:

2/20 - 2/24

Conditioning and stability, Regularization,

Variational and Iterative Techniques



Blahut 11.9


Week 7:

2/27 - 3/3

Sparsity-promoting regularization and machine learning methods




Week 8:

3/6 - 3/10

Physics of image formation: optical imaging

Midterm Exam


  Blahut 4.1-4.5, 4.7, 4.8  

Week 9:

3/20 - 3/24

Project Description

Principles of Range-Doppler radar and lidar

Project Guide

Paper List  

Journal Review/

Project proposal

Week 10:

3/27 - 3/31
Principles of Range-Doppler radar and lidar


  Blahut 6.1-6.7, 4.7, 4.8  

Week 11:

4/3 - 4/7
Synthetic-Aperture Radar


  Blahut 7.5-7.7  

Week 12:

4/10 - 4/14

Progress Presentations



Week 13:

4/17 - 4/21
Interferometric Radio Astronomy





Week 14:

4/24 - 4/28
Project Presentations        

Week 15:


Project Presentations

5/2: Last day of instruction