EEE 507: Multidimensional Signal Processing

Text: Dudgeon and Mersereau, Multidimensional Digital Signal Processing, Prentice-Hall, 1984.

Prerequisite: EEE 407 Digital Signal Processing, or equivalent.

Description:

Introduction to the theory and applications of multi-dimensional digital signal processing. This course is concerned with understanding signals of more than one variable and with systems for processing them. The most common examples of these signals include images, video, and arrays of sensors commonly encountered in sonar and seismic exploration.

Outline

I. Multi-D Discrete-Time(Space) Signals and Systems

A. Representation of Multi-D Signals, Special 2-D Sequences
B. Multi-D Linear Shift-Invariant Systems, Discrete Convolution
C. Implementation and Computational Cost
II. Multi-D Sampling
A. Rectangular Sampling
B. General Periodic Multi-D Sampling
C. Processing Signals Sampled on Arbitrary Lattices
III. Multi-D Discrete Fourier Transform (DFT)
A. Rectangular Discrete Fourier Transform
B. Circular Convolution
C. Implementations, Computational Complexity, and Storage Issues
D. General DFT for Signals Sampled on Arbitrary Lattices
IV. Multi-D Finite Impulse Response (FIR) Digital Filters
A. Direct Implementation, DFT-based Implementation, Block Processing
B. Design Techniques
V. Multi-D Infinite Impulse Response (IIR) Digital Filters
A. Two-D Difference Equations, Recursive Computability
B. Two-D Z-Transform, System Functions, Stability Analysis
C. Implementations and Filter Structures
D. Design Techniques
VI. Processing of Propagating Space-Time Signals
A. Space-Time Signals, Plane Waves, and Space-Time Filtering
B. Array Processing, Beamforming
C. Seismic Migration, Geophysical Processing
VII. Multi-D Signal Restoration and Reconstruction
A. Inverse and Wiener Filtering
B. Successive Approximation, Constrained Restoration
C. Reconstruction from Projections, Back-Projection Algorithm
D. Reconstruction from Phase or Magnitude

 
Course Coordinator: Lina Karam

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