BME 265: Digital Image Processing (Spring 2010)

NOTE: AS OF 11/05/2009, THIS CLASS IS FULL. YOU MAY CONTACT THE INSTRUCTOR TO BE ON THE WAITING LIST.  

Instructor: Prof. Sina Farsiu

Time: Mon-Wed 10:05-11:20 AM

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 171 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, 3rd edition, by R. Gonzalez and R. Woods, 2008. ISBN number 9780131687288. 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: Homework (15%), a midterm exam (25%), and a final project (60%).

Midterm exam will include a one-to-one interview with the professor. The material covered after the midterm exam will 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.

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 19-21                                       Image Segmentation

Lectures 22-23                                       Lossless and Lossy Compression

Lectures 24-25                                       Multi-Resolution Representation and Wavelets

Lectures 26-27                                       Sparse Representation and Compressive Sensing

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