
Instructor:
Prof. Gene
Cheung
Lecures:
TR 14:3016:00
Location:
CC 211
Announcement
 12/20/2019: Course homepage online.
Course
Summary
Fundamental
image processing theories and algorithms. Signal representation using
transforms, wavelets and frames is overviewed. Signal reconstruction
methods using total variation, sparse coding and lowrank prior, based
on convex optimization, are discussed. Applications include image
compression, restoration and enhancement. Prior background in digital
signal processing (EECS 4452 or equivalent) and numerical linear
algebra is strongly recommended.
Required
Textbook
 R. Gonzalez, R. Woods, Digital Image Processing (4th
Edition), Pearson Education Limited, 2018.
Supplementary
Material

M. Vetterli, J. Kovacevic, V. Goyal, Foundations of Signal Processing,
Cambridge University Press, 2014. (also available online HERE)
 M. Elad, Sparse
and Redundant Representations, Springer, 2010.
 A. Ortega, Graph
Signal Processing: An Introduction, to be published by Cambridge
University Press, 2020.
 S. Boyd, L. Vandenberghe, Introduction to Applied Linear Algebra:
Vectors, Matrices, and Least Squares, Cambridge University
Press, 2018.
Key
References
 A. Beck, M. Teboulle, "Fast GradientBased Algorithms for
Constrained Total Variation Image Denoising and Deblurring," IEEE Transactions on Image Processing,
vol. 18, no. 11, November 2009, pp. 24192434.

M.
Aharon, M. Elad, A. Bruckstein, "KSVD:
An Algorithm for designing
overcomplete sparse representation," IEEE Transactions on Signal
Processing, vol. 54, no. 11, Nov. 2006, pp. 43114322.

E.
Candes, X. Li, Y. Ma, J.
Wright, "Robust Principal Component Analysis?"
vol. 58, no. 3, article 11, Journal of the ACM, May 2011.

S.
Boyd et al., "Distributed
Optimization and Statistical Learning via the Alternating Direction
Method of
Multipliers," Foundation and Trends in Machine Learning,
vol.
3, no. 1, January 2011, pp.1122.

N.
Parikh and S. Boyd, "Proximal Algorithms," Foundations and
Trends in Optimization, vol. 1, no. 3, 2013, pp. 127239.
 J.
Han, A. Saxena, V. Melkote, K. Rose, "Jointly
Optimized Spatial Prediction and Block Transform for Video and Image
Coding," IEEE Transactions on
Image Processing, vol.21, no.4, April 2012, pp. 18741884.
 A.
Ortega et al., "Graph Signal
Processing: Overview, Challenges, and Applications," Proceedings of the IEEE, vol. 106,
no.5, May 2018, pp. 808828.
 G.
Cheung et al., "Graph Spectral Image
Processing," Proceedings of
the IEEE, vol. 106, no. 5, May 2018, pp. 907930.

W.
Hu, G. Cheung, A. Ortega,
O. Au, "Multiresolution Graph Fourier Transform for Compression of
Piecewise Smooth Images," IEEE Transactions on Image Processing,
vol.24, no.1, pp.419433, January 2015.

J.
Pang, G. Cheung, "Graph
Laplacian Regularization for Image Denoising: Analysis in the
Continuous Domain,"
IEEE Transactions on Image Processing,
vol. 26, no.4, April, 2017, pp.
17701785. (arXiv)
Evaluation
 Biweekly assignments (40%)
 Midterm (30%)
 Course project (30%)
Course
Outline (subject to change)
 Week 1: Linear Algebra Review
 Week 2: Innerproduct, Hilbert Space
 Week 3: Image Analysis: Transforms
 Week 4: Image Analysis: Wavelets
 Week 5: Sparse / LowRank Signal Representations
 Week 6: Image / Video Compression
 Week 7: Image Restoration: Denoising and
Deblurring
 Week 8: Graph Spectral Image Compression
 Week 9: Graph Spectral Image Processing
 Week 10: Graphbased 3D Point Cloud Processing
 Week 11: Graph Neural Networks for Image
Processing

last modified March 5, 2020


