E9 241: Digital Image Processing


Announcements:

  1. Final Exam: 8/12 (Monday) from 10:00 - 12:00 in EE 303.

  2. Project Viva: 27-28 November.

  3. The second mid-term test will be on 2/11 from 4:00 - 5:30 PM in EE 303.

  4. Deadline for project report: 24/11.

  5. Final assignment A8 posted, due on 27/9.

  6. Assignment 7 posted, due on 15/10.

  7. The first mid-term test will be on 27/9 from 4:00 - 5:30 PM in EE 303.

  8. Assignment 6 posted, due on 6/10.

  9. Assignment 5 posted, due on 22/9.

  10. Assignment 4 posted, due on 15/9.

  11. Assignment 3 posted, due on 1/9.

  12. Assignment 2 posted, due on 25/8.

  13. TA: Konda Reddy Mopuri (SERC) and Sk. Mohammadul Haque (EE).

  14. Assignment 1 posted, due on 18/8 at the start of the lecture.

  15. The first class will be in EE 303 on 4th August from 10 - 11 AM.

Course Details

  • Term: August - December 2014.

  • Credits: 2:1.

  • Hours: 10 - 11 AM, Monday and Wednesday.

  • Webpage: tinyurl.com/y39k42b8

  • Instructor: Kunal Chaudhury (kunal@ee.iisc.ernet.in).

  • Venue: EE 303.

  • Prerequisites: Basics of linear algebra and signals and systems, and some programming skills. Knowledge of probability and optimization would help, but is not mandatory.

  • Course structure: About 34 hours of instruction, mid-term exams, exercise series (math problems and MATLAB assignments), mini-project, and final exam. The project would require the students to read, understand, and implement the (basic) algorithm from a recent paper on one of the topics mentioned above (group work will be allowed).

  • Grading: Mid-terms: 15%, Exercises: 15%, Project: 20%, Final exam: 50%.

  • Description: The focus of the course would be on basic concepts and ideas in image processing. This is a big area, so we will eventually have to focus on specific topics. The main topics for this course will be image transforms and image restoration (denoising, deblurring, super-resolution, etc.), and related computational aspects. I will try to provide a good mix of both classical methods and modern state-of-the-art techniques. Most of the things will be explained on the blackboard and using Matlab demos; I will avoid using slides as much as possible.

  • Notes/Slides: I will post slides on this page that give will give a broad overview of each lecture and the topics covered. The details will be worked out on the blackboard during the lecture.

  • References:

  1. Mallat, A Wavelet Tour of Signal Processing.

  2. Jain, Fundamentals of Digital Image Processing.

  3. Gonzalez et al., Digital Image Processing using MATLAB.

  4. Strang, Linear Algebra and its Applications.

  5. Boyd and Vandenberghe, Convex Optimization. [pdf]

  • Suggested Readings:

  1. Cooley and Tukey (1965). An algorithm for the machine calculation of complex Fourier series.

  2. Deriche (1993). Recursively implementing the Gaussian and its derivatives.

  3. Turk and Pentland (1991). Face recognition using eigenfaces.

  4. Milanfar (2013). A tour of modern image filtering: new insights and methods, both practical and theoretical.

  5. Strang (1999). The Discrete Cosine Transform.

  6. Kovesi. MATLAB and Octave functions for computer vision and image processing [pdf].

  7. Unser. Splines: A unifying framework for image processing. [pdf]

  8. Bouman. Grand challenges in image processing. [pdf]

  • Journals on Image Processing:

  1. IEEE Transaction on Image Processing [link].

  2. IEEE Transaction on Signal Processing [link].

  3. IEEE Signal Processing Letters [link].

  4. SIAM Journal on Imaging Sciences [link].

  5. IET Image Processing [link].

  6. Signal Processing [link].

  7. Image Processing Online [link].