| Instructor: | Prof. Charles A. Bouman |
| Office: | MSEE 320 |
| Phone: | (765) 494-0340 |
| E-mail: | bouman@purdue.edu |
| Course Hours: | MWF 1:30–2:20 pm |
| Classroom: | Brown Family Hall (BHEE) 226 |
| Office Hours: | Immediately after class (MWF) |
| Class TA: | Jingsong Lin (voluntary), lin1311@purdue.edu |
| TA Hours: | Thursdays 3:00–5:00 pm, starting Sept. 3 |
| TA Location: | Zoom, Meeting ID 286 413 2875 |
| Course Web Page: | engineering.purdue.edu/~bouman/ece60141 |
| Lab Web Page: | cabouman.github.io/grad_labs |
C. A. Bouman, Foundations of Computational Imaging: A Model-Based Approach, SIAM, 2022. ISBN 978-1-611977-12-7. See the book page for student discounts.
An advanced treatment of the methods in model-based signal and image processing including stochastic modeling of multidimensional signals, Bayesian estimation, inverse methods, doubly stochastic models, regularized inversion, the EM algorithm, Bayesian networks, Markov chains, optimization, convexity, majorization techniques, and stochastic simulation. The underlying theory is presented in the context of applications including image restoration, tomographic reconstruction, clustering, classification, and segmentation.
The course presents the basic analytical and algorithmic tools used for processing information from a wide variety of physical sensors and applications ranging from medical CT scanners to speech signals. The basic theme of the course is the formulation of signal processing problems as inverse problems, and the solutions of inverse problems using the techniques of signal and system modeling along with parameter and signal estimation. The course also incorporates a number of computer-based laboratory exercises so that students can better understand how to implement the methods discussed in the class. The course is intended to be accessible to students with a variety of applications backgrounds, but they should have basic familiarity with probability, random variables, and random processes at the level of ECE 600 or ECE 302.
Final grades will use the following weighting.
| Homework | 15% |
| Laboratories | 15% |
| Midterm | 30% |
| Final exam | 40% |
This class is graded on a curve.
Submit all assignments and labs in PDF format via the Brightspace web page. Clearly label each submission (for example, “Homework 1” or “Lab 2”). The submitted PDF should include all requested code and results.
AI tools are permitted for any course work except in-class closed-book exams. Any use of AI must be disclosed.