Faculty of Information Technology, BUT

Course details

Advanced Methods of Digital Image Processing

QB5 Acad. year 2007/2008 Summer semester

Introduction into theory of multidimensional signals, explanation of theoretical principles of methods of formalised image restoration, of image reconstruction from projections and of methods based on disparity analysis. Formalised image segmentation and methods of object recognition.


Language of instruction



Examination (oral)

Time span

39 hrs lectures

Assessment points

100 exam



Subject specific learning outcomes and competences

Deeper insight into advanced methods of image data processing, abilities to apply the methods and, if needed, to modify them for a concrete problem.

Learning objectives

Providing deeper knowledge of theoretically demanding methods of image data processing and of their applications.

Prerequisite kwnowledge and skills

Knowledge of signal processing.

Syllabus of lectures

  1. Concepts of advanced image processing methods. Overview of the theory of 2D signals and 2D transforms, image as a realisation of a 2D stochastic field.
  2. Discrete image representation, discrete linear and non-linear 2D operators, neural 2D filters.
  3. Formalised image restoration - concepts, identification of deterioration and noise. Pseudoinversion, Wiener filtering via frequency domain.
  4. Image restoration by constrained deconvolution method. Method of maximum entropy.
  5. Generalised discrete LMS method, method of impulse response optimisation, approaches based on maximum posterior probability.
  6. Image restoration by neural networks using iterative optimisation of network "energy", comparison with classical approaches.
  7. Radon transform and projection tomography, image reconstruction from projections. Algebraic iterative methods of reconstruction.
  8. Projection-slice theorem, reconstruction from projections via frequency domain. Image reconstruction by filtered back-projection. Generalisation of methods for fan-projections.
  9. Disparity analysis and pair-wise image comparison. Movement analysis.
  10. 3D surface reconstruction based on disparity analysis of stereo-pairs.
  11. Formalised image segmentation, texture analysis, prior-knowledge based segmentation.
  12. Object contour restoration, Hough transform. Morfological transforms.
  13. Object recognition in images by means of learning neural networks, comparison with feature based recognition procedures using cluster analysis.

Progress assessment

Doctoral course: discussions.

Course inclusion in study plans

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