E9 241-O Digital Image Processing 3:1 (August 2021)

Course Instructor: Chandra Sekhar Seelamantula, EE

Course description: This is a foundational course in digital image processing and has a significant programming component that augments the concepts taught in the theory classes. This course is essential for all those who wish to pursue advanced research in Computational Imaging, Computer Vision, and Machine Learning using images.

Syllabus

Introduction to image processing; Image acquisition, image representation; Quantization, optimal thresholding, binarization, halftoning; Image histogram, histogram equalization, sharpening; Sampling, aliasing; Fourier transform, magnitude spectrum, phase spectrum, properties; Splines - continuous and discrete image processing; Image filtering - Gaussian smoothing, Bilateral filtering, nonlocal means filtering; Morphological filters; Directional image processing; Image interpolation; Edge detection; Principal component analysis; Pyramidal representation - Gaussian and Laplacian pyramids; Multiscale and multiresolution representation - filterbanks and wavelet representations; Image denoising; Image deconvolution; Iterative algorithms for image restoration, wavelet-based algorithms for image restoration; Sparse representations in image processing, dictionary learning; Image super-resolution; Introduction to Radon transform and computerized tomography; Introduction to convolutional neural networks (CNNs) for image processing.

Textbooks / References

  1. R. Gonzalez and R. Woods, Digital Image Processing, Pearson International, 4th edition, 2018.
  2. A. Bovik, The Essential Guide to Image Processing, Academic Press, 2009.

Prerequisites: None

Grading:

  • Homework (assigned once in two weeks) 40%
  • Midterm 30%
  • Final exam 30%.