Sina Farsiu

BME 271: Signals and Systems (Spring 2015, Spring 2016, Spring 2018)

NOTE#1: ALL COURSE ANNOUNCEMENTS, LECTURE NOTES, HOMEWORK, UPDATES, AND INFORMATION WILL BE DISTRIBUTED ONLY THROUGH DUKE'S SAKAI SOFTWARE. ALL ENROLLED STUDENTS ARE REQUIRED TO USE THIS SOFTWARE.

BME 544 - 01: Digital Image Processing (Spring 2011,Spring 2012, Spring 2013, Spring 2014, Fall 2017, Spring 2019, Fall 2020)

NOTE#1: ALL COURSE ANNOUNCEMENTS, LECTURE NOTES, HOMEWORK, UPDATES, AND INFORMATION WILL BE DISTRIBUTED ONLY THROUGH DUKE'S SAKAI SOFTWARE. ALL ENROLLED STUDENTS ARE REQUIRED TO USE THIS SOFTWARE.
NOTE #2: Based on the previous years experience, regretfully, there is a chance of receiving a low grade if the student  A) does not attend all lectures "on time" and/or B)  does not spend a few hours every week on his/her final project.

TENTATIVE COURSE INFORMATION:

Instructor: Prof. Sina Farsiu

Time: Wed-Fri 10:05-11:20 AM,

Permission code: Please directly contact Ms. Susan Story-Hill (sshstory@duke.edu) to receive permission code.

Room: TBD

Course Description: Introduction to the theory and methods for digital image sampling, enhancement, visualization, storage, reconstruction, and analysis with emphasis on medical applications. This course is mainly designed for BME graduate students. However, graduate students from all engineering disciplines, computer science, and senior undergraduate students who have already passed required courses may also participate.

Prerequisites: The student must have passed one undergraduate course on signals and systems and one course on probability and statistics:

A (Probability and Statistics): MATH 135 or STA 113 or ECE 255 and EE 64 or permission of instructor.

B (Signals and Systems): BME 571 or ECE 54 or permission of instructor (if you find this comic strip funny " link", you probably know enough about signals and systems).

The students must also have a basic knowledge of the MATLAB software. 

Text: Digital Image Processing, XX edition, by R. Gonzalez and R. Woods, 20XX. ISBN number XXX. Although, several lectures are loosely based on the text book material, for which handouts and journal articles will be provided by the lecturer.

Student Evaluation (MORE DETAILS IN THE BLACKBOARD POSTINGS): Homework (15%), a midterm exam (35%), and a final project (50%).

Midterm exam will include a one-to-one interview with the professor. The material covered after the midterm exam might be questioned at the final presentation. The final project will include a class presentation at the end of the semester, an unlimited page report due one week before the-end-of-the-term presentation, and one four-page paper following the style of the IEEE International Conference on Image Processing (ICIP) papers due on the day (and in lieu) of the final exam. Final projects can be done individually or in a group, however, each student must have a defined role approved by the professor. Professor will help in project selection. Homework, mfiles, and reports must be submitted electronically by 10:00AM of the deadline unless stated otherwise by the lecturer(late submissions are not accepted).

Course Objectives: The student will gain a basic knowledge of the most fundamental issues as well as novel topics in image processing. By the end of this course, he/she should have a comprehensive knowledge of an image processing topic, based on the final term project of his/her choice, and should be able to take over the image processing tasks in his/her academic career with minor required supervision.   

Tentative Course Outline:

Lecture 1                                             Introduction, history, applications, and fundamentals of Image Processing.

Lectures 2-4                                        Spatial Domain Image Enhancement: Denoising and Contrast enhancement

Lectures 5-7                                        Fourier Domain Image Representation and Enhancement

Lectures 8-10                                      Registration: Optical Flow and Phase-Based Motion Estimation

Lectures 11-16                                     Inverse Problems (Wiener filter, Least-squares, Denoising, Deblurring, Blind Deblurring, Back Projection, and Reconstruction)

Lectures 17-18                                    Interpolation (Single-Frame, Multi-Frame, Super-Resolution)

Lectures 19-22                                    Multi-Resolution Representation, Wavelets, Sparse Representation and Compressive Sensing

Lectures 23                                         Biological Image Processing: Vision and Art

Lectures XXX Student presentations of final projects (in case of high enrollment in this course, some presentations will be scheduled for the last two weekends).
Last lecture:                                        The real world is not fair and in general cannot be modeled as "linear", or "Gaussian", and how to deal with it.