EE 264 - Image Processing and Reconstruction

Spring 2008

Instructor:

Peyman Milanfar

Office:

Engineering II , Room 243A

Phone:

(831) 459-4929

email:

milanfar AT ee DOT ucsc DOT edu

Lecture:

T/Th 12:00 to 1:45, Telecast to/from BE 156 & SVC 2073

Office Hours:

T/Th 2 to 3

Required Text:

Digital Image Processing by Gonzalez and Woods, Second Edition (Errata)

Optional Reference Texts:
  • Digital Image Processing Using MATLAB by Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins
  • Digital Image Processing by William K. Pratt
  • Fundamentals of Digital Image Processing by Anil K. Jain
  • Two-Dimensional Imaging by Ronald N. Bracewell

Grading Policy:

Homeworks (20%), Midterm (30%), Final Project (50%)

Notes:

Homework exercises will require the use of the software package MATLAB. Here is a primer.

 

Important Dates:

First day of class

Tuesday, April 1

Last day of class

Thursday, June 5

Midterm examination

Tuesday, May 6

Final Project presentations

Thursday, June 5 12-3:30 PM in BE 156

Final Project reports and code due

Wed, June 11

 

Lecture Notes:

  1. Overview
  2. Review
  3. Image Formation, Sampling, and Resolution
    1. The Myth of Megapixels (NY Times article by David Pogue)
  4. Point-wise Operations
    1. Retinex
    2. Histogram Equalization Derivation
    3. Optimal Averaging Derivation
  5. Local Operations in the Image Domain
  6. 2-D Signals and Systems, Matrix Formulations
  7. The Frequency Domain
  8. Filtering in the Freq. Domain, Sampling.
  9. Some additional notes on the frequency domain
  10. Restoration I: Description and basics of freq. domain approach
  11. Restoration II: Power Spectra and the Wiener Filter
  12. Restoration III: Basics of Pixel-domain Restoration and Statistical Methods
    1. The SVD and SVD-based regularization
    2. ML/MAP and Weighted MAP
  13. Restoration IV: Advanced Pixel Domain Restoration, Implementation
  14. Motion Estimation I: Intro, models and block-based matching
  15. Motion Estimation II: Optical Flow motion estimation
  16. Multiresolution Image Processing
  17. Introduction to Compression
    1. Video Coding
  18. Indirect Imaging: Tomography
  19. Introduction to Image Analysis
  20. Color Imaging
  21. Kernel and Nonlinear Filtering Methods
  22. Midterm Review Notes
  23. Last Year's Midterm with Solutions

 

 

 

  • Link to Lecture Recordings is Here
  • My Tentative Location Schedule (subject to change):
    • Tuesday April 15: @ SVC
    • Thursday April 17: on Campus
    • Tuesday April 22: on Campus
    • Thursday April 24: @ SVC
    • Tuesday April 29 : on Campus
    • Thursday May 1: on Campus
    • Tuesday May 6: @ SVC (Midterm day)
    • Thursday May 8: on Campus
    • Tuesday May 13: on Campus
    • Thursday May 15: on Campus
    • Tuesday May 20: @ SVC
    • Thursday May 22: on Campus
    • Tuesday May 27: on Campus (Guest Lecture)
    • Thursday May 29: on Campus
    • Tuesday June 3: on Campus
    • Thursday June 5: on Campus (Final Presentations)

 Homeworks:

Term Project:

Links and Matlab Demos:

Tentative Syllabus and Reading: 

 

 

Academic Dishonesty and Cheating:

Any confirmed academic dishonesty including but not limited to copying homeworks or cheating on exams, will result in a no-pass or failing grade. You are encouraged to read the campus policies regarding academic integrity. Examples of cheating include (but are not limited to):

  • Sharing or copying results or other information during an examination.
  • Working on an exam before or after the official time allowed.
  • Submitting homework that is not your own work.
  • Reading another student's homework solution before it is due.
  • Allowing someone else to read your homework solution before the assignment is due.

If there is any question as to whether a given action might be construed as cheating, see me before you engage in any such action.