Advanced Image Processing

Spring 2009

Lecture: Thursday, 14:15-16:00, Battelle (Bat 316-318) ( beginning February 19 )

Labs: Wednesday, 14:15-16:00, Battelle (Bat 314-315) (beginning February 18)

CUI - University of Geneva, Computer Vision Multimedia Laboratory


Sviatoslav Voloshynovskiy (Office 215, Office hours: Tuesday 11.00-12.00 or according to appointment by email)
Stephane Marchand-Maillet (Office 212, Office hours: Tuesday 11.00-12.00 or according to appointment by email)
Teaching Assistant:
Taras Holotyak (Office 226)
Battelle Building A,
7, route de Drize,
1227 Carouge
CUI, University of Geneva
If you are interested in this course and wish to be added to mailing list, please, send an email.

Course Outline

  • Recall of Linear Algebra. Multidimensional Signal Processing ( ~1.5 lectures)

  • Vector and matrix image presentations, discrete and continuous Fourier transforms.

  • Introduction. Human Visual System ( ~0.5 lecture)

  • Modulation transfer function, visual masking, noise visibility, color vision;
    Distortion measures.

  • Image Representation : pyramids and wavelets ( ~1 lecture)

  • Random signals ( ~1 lecture)

  • Image Modeling ( ~3 lectures)

  • Stochastic presentation of images;
    Stationary continuous- and discrete-space models, including AR, MRF, stationary Generalized Gaussian;
    Nonstationary models: non-stationary Gaussian, HMM;
    Transform-based models (DFT, DCT, wavelet);
    Edge and texture models;
    Doubly stochastic processes;
    Relationships between models.

  • Image Sensor Models ( ~0.5 lecture)

  • Optical, radar and medical coherent/noncoherent imaging applications:
    (aperture difraction constrains, defocusing, motion blur, atmospheric turbulence, sparse imaging apertures);
    Photographic film;
    Electronic imaging;
    CCD imaging applications;
    Smart sensors.

  • Noise Models ( ~0.5 lecture)

  • Additive noise: Poisson, Gaussian and Laplacian models;
    Multiplecative noise: speckle model.

  • Image Denoising ( ~1 lecture)

  • Maximum-likelihood estimation;
    Bayesian estimators;
    Models selection (MDL principle);
    Transform-based denoising: adaptive Wiener filtering, soft-shrinkage and hard-thresholding.

  • Image Restoration ( ~1 lecture)

  • Statistical ill-posed problems;
    Deterministic regularization: Tikhonov, edge-preserving and adaptive regularizations;
    Transform-based restoration;
    Blind deconvolution.

  • Image Compression ( ~1 lecture)

  • Basics of source coding theory (lossless and lossy);
    Vector quantization, codebook design;
    Transform and subband coding;
    Relationship between compression and denoising.

  • Video Modeling and Compression ( ~1 lecture)

  • 3-D and 2-D Motion models;
    Block matching (simple, hierarchical, and overlapped), optical flow;
    Transform-based models;
    Motion-compensated prediction models;
    Transform and motion-based compression techniques;
    Bidirectional prediction.

  • Digital Data Hiding ( ~2 lectures)

  • Steganography (secure communications);
    Digital watermarking: fundamentals, channel coding, masking, robustness against geometrical transforms and applications
    (robust watermarking, tamper proofing and self-recovering, document authentication, access control, indexing).

    Prerequisites: Imaginarie Numerique (Prof. Thierry Pun), good knowledge of Matlab.

    Supplementary Materials:
    • A.K.Jain, Fundamentals of Digital Image Processing, Prentice-Hall, 1989.
    • A.M.Tekalp, Digital Video Processing, Prentice-Hall, 1995.
    • A.Bovik, Handbook of Image Video Processing, Academic Press, 2000.
    • H.Stark and J.W.Woods, Probability, Random Processes, and Estimation Theory for Engineers, Prentice-Hall, 1994.
    • A.M.Yaglom, Correlation Theory of Stationary and Related Random Functions I: Basic Results, Springer-Verlag, 1987.
    • L.Breiman, Probability, SIAM, 1992.
    • H.V.Poor, An Introduction to Signal Detection and Estimation, 2nd Ed., Springer-Verlag, 1994.
    • A.Gersho and R.M.Gray, Vector Quantization and Signal Compression, Kluwer, 1992.
    • M.Vetterli and J.Kovacevic, Wavelets and Subband Coding, Prentice-Hall, 1995.


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