Advance Image Processing

Course Objective: After completion of this training, participants will be able understand the concept of Image modeling, image sensor models and noise models, understand image denoising, image compression and image restoration concepts.

Who Should Attend?
Advance Image Processing course is specially designed for the R&D professionals working in Telecom, Software development or defense industry.

Training Contents:

Multidimensional Signal Processing:
Vector and matrix image presentations, discrete and continuous Fourier transforms.

Introduction. Human Visual System
Modulation Transfer Function, Visual Masking, Noise Visibility, Color Vision; Distortion measures.

Image Representation : pyramids and wavelets

Random signals

Image Modeling

• Stochastic presentation of images.

• Stationary continuous- and discrete-space models, including AR, MRF, stationary Generalized Gaussian:

• non-stationary models: non-stationary Gaussian, HMM:

• transform-based models (DFT, DCT, wavelet);

• edge and texture models;

• doubly stochastic processes;

• relationships between models.

Image Sensor Models

• optical, radar and medical coherent/non-coherent imaging applications: (aperture diffraction constrains, defocusing, motion blur, atmospheric turbulence, sparse imaging apertures)

• photographic film;

• electronic imaging;

• CCD imaging applications;

• smart sensors.

Noise Models

• additive noise: Poisson, Gaussian and Laplacian models;

• multiplecative noise: speckle model.

Image Denoising

• Maximum-likelihood estimation;

• Bayesian estimators;

• models selection (MDL);

• transform-based denoising: adaptive Wiener filtering, soft-shrinkage and hard-thresholding.

Image Restoration

• statistical ill-posed problems;

• deterministic regularization: Tikhonov, edge-preserving and adaptive regularizations;

• transform-based restoration;

• blind deconvolution.

Image Compression

• 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

• 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

• 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).