NYU-Poly, Electrical & Computer Engineering
EL5123 /BE6223 ---- Image Processing, Fall 2011

Course Description:  This course introduces basic concepts and techniques in digital image processing: image acquisition and display using digital devices, properties of human visual perception, sampling and quantization, sampling rate conversion, contrast enhancement, two-dimensional Fourier transforms, linear and nonlinear filtering, morphological operations, noise removal, image deblurring, edge detection, image registration and geometric transformation,  and multiresolution representation using wavelets, and image compression (including the JPEG and JPEG2000 standard). Students will learn to implement some image processing algorithms on computers using C-programming or MATLAB.

Prerequisites: EE 3054 (Signals, Systems, and Transforms), Knowledge of basic matrix operations and probability; basic programming skill; senior or graduate student status. This course can be used to form a two-course sequence with EL6122 or EL5823.

Course Instructor:

Yao Wang, Office: LC256, email: yao at poly.edu, homepage: http://eeweb.poly.edu/~yao


Wed. 3:00 - 5:40 PM, Room: RH615

Office Hour:

LC256, Mon. 4:30-5:30, Wed. 10:00-11:00, Thur. 10:00-11:00 or by appointment.

Lab room and times: Multimedia Lab (LC008), check lab open hour at http://eeweb.poly.edu/~yao/EL5143

Text Book: R. C. Gonzalez and R. E. Woods, Digital Image Processing, Prentice Hall, (3rd Edition) 2008.  (If you already have the 2nd ed, you can use it.)

Recommended Readings: A. K. Jain, Fundamentals of Digital Image Processing, Prentice Hall, 1989 (for more mathematical and comprehensive treatment than Gonzalez and Woods) (available at Poly Library)

Homework Policy: Weekly written and/or computer programming assignment, due the following week or as specified. (Late submission will not be accepted.

Grading Policy: Exam 1 40%, Exam 2 (non-cumulative) 40%, Homework 20% (Programming assignment 10%, others 10%)

Course Schedule

  • 9/7: Lecture 1: Overview of basic image processing techniques and their applications; Image formation and perception; digital image representation; Matrix algebra review, Matlab review.  Lecture note (updated 9/6/2011) (note: HW assignment is in lecture note)
  • 9/14: Lecture 2: Image quantization: uniform and nonuniform, visual quantization (dithering); color coordinate and conversion; color image quantization. Lecture note (updated 9/20/11) (note: HW assignment is in lecture note)
  • 9/21: Lecture 3: Image contrast enhancement. Lecture note (updated 9/20/11) (note: HW assignment is in lecture note).
  • 9/28: Lecture 4: Discrete-time Fourier Transforms (DTFT) in 2D;  2 D convolution. Interpretation of spatial domain filtering in frequency domain. Lecture note (updated 9/28/11) (note: HW assignment is in lecture note).
  • 10/5: Lecture 5: Image  smoothing and image sharpening by spatial domain linear filtering; Edge detection. Lecture note (note: HW assignment is in lecture note).
  • 10/12: Monday class meet. No lecture
  • 10/19: Lecture 6: Discrete Fourier transform (DFT) in 1D and 2D, and  image filtering in the DFT domain. Lecture note. 
  • 10/26: Lecture 7: Median filtering and Morphological filtering. Lecture note. Midterm Review Note (to be updated).
  • 11/2: Midterm Exam 
  • 11/9: Lecture 8: Image sampling and sampling rate conversion (resize). Lecture note.
  • 11/16: Lecture 9: Lossless image compression: The concept of entropy and Huffman coding; Runlength coding for bi-level images; CCITT facsimile compression standards. Lecture note.
  • 11/23: Lecture 10: Lossy image compression: Image quantization revisited; Predictive coding; Transform coding; JPEG image compression standard. Lecture note.
  • 11/30: Lecture 11: Wavelet transform; JPEG2000 image compression standard. Lecture note
  • 12/7: Lecture 12: Imaging Geometry; Coordinate transformation and geometric warping for image registration. Lecture note.
  • 12/14: Lecture 13: Image Restoration (denoising and deblurring). Lecture note.  Final review (lecture note)
  • 12/21:  Final Exam
  • Sample midterm exam (F05) with solution, Sample midterm exam (F08), solution to midterm F08,  sample final exam (F04) with solution. Another sample final exam (F05) (w/o solution), Final exam F08, Solution to final exam of F08; Final exam F09, Solution to final exam of F09 (please note the correction in the yellow sticker in the pdf file). Midterm exam (F10).



Last updated: 10/25/2011, Yao Wang