EE 264 - Image Processing and Reconstruction

Spring 2007

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, BE 156

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 3

Last day of class

Thursday, June 7

Holiday

Monday, May 28

Midterm examination

Tuesday, May 8

Final Project reports due

Thursday, Tuesday June 5

Final Project presentations

Thursday, June 7, 12-3 PM, Room E2-215

  • (NOTE DIFFERENT ROOM)
  • Attendance at all presentations is required of all students registered in the class.

 

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. Old Practice Midterm
  24. MIDTERM SOLUTIONS

Lecture Recordings (avi format)**:

  1. Lecture 1 (Overview, Review)
  2. Lecture 2 (Imaging and Resolution)
  3. Lecture 3 (Point processing)
  4. Lecture 4 (Local Operations, 2-D Sig+Sys)
  5. Lecture 5 (Intro to Freq Domain -- Sorry no Audio!)
  6. Lecture 6 (More on Freq Domain) 
  7. Lecture 7 (Restoration in the Freq Domain)
  8. Lecture 8 (Restoration in the Pixel Domain)
  9. Lecture 9 (SVD and SVD - based Regularization)
  10. Lecture 10 (Midterm Review and Matlab demos)
  11. Lecture 11 (Color Imaging)
  12. Lecture 12 (Motion Estimation I)
  13. Lecture 13 (Motion Estimation II)
  14. Lecture 14 (Multiscale Image Processing)
  15. Lecture 15 (Compression)
  16. Lecture 16 (Tomography)
  17. Lecture 17 (Kernel-based methods) 
  18. Lecture 18 (Intro to Image Analysis)
  19.  
  20. Final Project Presentations

**You may view these lectures at any time, but do not distribute them beyond the UCSC environment. These lectures have been created using the Camtasia software, and can be played through the Camtasia player software, downloadable for free from techsmith here, or through the standard windows media player with the techsmith codec. A Mac OSX version of the codec can be found here that allows playback of the files. Note that some students have reported that VLC works much better on MacOSX and Linux. Prof. Gabriel Elkaim's help with the recording technology is gratefully acknowledged.

 

 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.