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Instructor:
Prof.
Gene
Cheung
Lectures:
MW
13:00-14:30
Location:
CC
318
Announcement
- 12/23/2021: Course homepage online.
- 01/04/2022: eClass website visible to
registered students.
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
low-rank 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,
Introduction
to Graph
Signal Processing, Cambridge
University Press, 2022. (available soon in
Amazon HERE)
- G. Cheung,
E. Magli, Graph
Spectral Image Processing,
Wiley-ISTE, 2021. (available in Amazon HERE)
- 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
Gradient-Based Algorithms for
Constrained Total Variation Image
Denoising and Deblurring," IEEE
Transactions on Image Processing,
vol. 18, no. 11, November 2009, pp.
2419-2434.
-
M.
Aharon,
M. Elad, A. Bruckstein, "K-SVD:
An
Algorithm for designing
overcomplete sparse representation,"
IEEE
Transactions on Signal
Processing, vol. 54, no. 11, Nov.
2006, pp. 4311-4322.
-
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.1-122.
-
N.
Parikh
and S. Boyd, "Proximal Algorithms,"
Foundations and
Trends in Optimization, vol. 1, no.
3, 2013, pp. 127-239.
- 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. 1874--1884.
- A.
Ortega
et al., "Graph
Signal
Processing: Overview, Challenges, and
Applications," Proceedings
of the IEEE, vol. 106,
no.5, May 2018, pp. 808-828.
- G.
Cheung
et al., "Graph
Spectral Image
Processing," Proceedings
of
the IEEE, vol. 106, no. 5, May
2018, pp. 907-930.
-
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.419-433, 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.
1770-1785. (arXiv)
Evaluation
- Bi-weekly assignments (40%)
- Midterm (30%)
- Course project (30%)
Course
Outline
(subject to change)
- Week 1: Linear Algebra Review
- Week 2: Inner-product, Hilbert Space
- Week 3: Image Analysis: Transforms
- Week 4: Image Analysis: Wavelets
- Week 5: Sparse / Low-Rank Signal
Representations
- Week 6: Image / Video Compression
- Week 7: Reading Week
- Week 8: Image Denoising
- Week 9: Graph Spectral Image Compression
- Week 10: Graph Spectral Image Processing
- Week 11: Graph-based 3D Point Cloud Processing
- Week 12: Graph Neural Networks for Image
Processing
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last modified February 5, 2022
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