York University


EECS 6354 Digital Image Processing: Theory and Algorithms

(Winter 2020)



 
The Lassonde School of Engineering
Department of Electrical Engineering & Computer Science





Content starts here

Content starts here

Instructor: Prof. Gene Cheung

Lecures: TR 14:30-16:00

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 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, 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 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: Image Restoration: Denoising and Deblurring
  • Week 8: Graph Spectral Image Compression
  • Week 9: Graph Spectral Image Processing
  • Week 10: Graph-based 3D Point Cloud Processing
  • Week 11: Graph Neural Networks for Image Processing



last modified March 5, 2020

 

 

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