ECE 558: Digital Imaging

Spring 2017

Course Information:

ECE 558 is a graduate course on multidimensional signals, convolution, transforms, sampling, and interpolation; design of two-dimensional digital filters; sensor array processing and range-doppler imaging; applications to synthetic aperture radar, optics, tomography, radio astronomy, and beam-forming sonar; image estimation from partial data.
 
Prerequisite: ECE 310 (Digital Signal Processing) and ECE 313 (Probability with Engineering Applications), or equivalent.
 
Instructor:  Prof. Zhizhen Zhao, 324 CSL, e-mail: zhizhenz AT illinois dot edu

Time and Location: 11:00 am -- 12:20 pm, Tuesdays and Thursdays, ECEB 3013
 
Texts: R. E. Blahut, Theory of Remote Image Formation, 2004.  
Optional reading: T. K. Moon and W. C. Stirling, Mathematical Methods and Algorithms for Signal Processing.
 
Announcement: The mid-term exam is changed to Thursday, April 13
 
Grading:
 Please check the syllabus for additional information.
 
Lecture Notes: 
Lecture 1: Introduction and 2D Fourier Transform
Lecture 2: Examples of 2D Fourier Transform
Lecture 3: Resolution and Projection-Slice Theorem
Lecture 4: Sampling Theory, DFT and FFT
Lecture 5: Fast Polar Fourier Transform and also the paper by Averbuch et al and another more recent paper on direct inversion of the 3D pseudo-polar Fourier transform.
Lecture 8-9: Optical Imaging System
Lecture 10: Antenna and Radar imaging.  Reading: Some applications of Synthetic Aperture Radar and Blahut Chap. 5.1. 
Lecture 11-12: Reciprocity and Antenna array (Blahut Chap. 5.2-5.3); Range-Doppler and Ambiguity Function (Blahut Chap. 6).
Lecture 13-14: Tomographic Inversion (Blahut). Iterative methods. Application in Cryo-EM.
Lecture 15-18: Denoising, Sparse Signal Recovery.
Lecture 19: Principal Component Analysis. Additional reference.
Lecture 20: Wiener Filtering.
Lecture 21-22: Random Projection and Compressive Sampling (Lecture notes by Dr. Fernandez-granda). Message-passing algorithms for compressed sensing (Donoho, et. al.).
Lecture 25: Superresolutoin slides
Lecture 26-27: Learning representation and low rank models: Slide 1, Slide 2 

Homeworks: 

Assignment 1 Solutions

Assignment 2 Solutions

Assignment 3 Solutions

Assignment 4 Solutions

Assignment 5 Solutions

Assignment 6 Solutions

Journal review and Projects:

The suggested papers are listed here. If you have trouble finding the papers, please email me.

Guide for journal review and projects: Project Guide

Journal Review, Proposal template: Template 

 

Past Exam:  2015