PA171 Digital Image Filtering

Faculty of Informatics
Autumn 2021
Extent and Intensity
2/2. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Taught online.
doc. RNDr. David Svoboda, Ph.D. (lecturer)
Guaranteed by
doc. RNDr. David Svoboda, Ph.D.
Department of Visual Computing - Faculty of Informatics
Contact Person: doc. RNDr. David Svoboda, Ph.D.
Supplier department: Department of Visual Computing - Faculty of Informatics
PV131 Digital Image Processing
Knowledge of written English and calculus is required.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 49 fields of study the course is directly associated with, display
Course objectives
The aim of this lecture is to introduce all the basic image transforms used in digital image processing. It covers the operations of changing the image content or transforming the original data into a different representation. At the end of this course, students should be able to:
- understand the basic principles of the image transforms;
- know the selected transforms;
- implement and apply the selected transforms;
- understand standard image compression algorithms;
- correctly resample images;
- use suitable image restoration algorithms.
Learning outcomes
After completing the course, the student should be able to:
- analyze the image data in a frequency domain;
- discuss the problems in the field of frequency analysis;
- propose her/his own efficient and optimized compression methods;
- demonstrate the general principles of compression algorithms;
- use wavelet and Fourier transform appropriately;
- solve the tasks focused on image restoration;
- appropriately use the resampling algorithms and understand their results
  • Discrete transforms (Fourier transform, FFT, Hadamard, DCT, Wavelets)
  • Image compression, Lossy/Lossless compression, JPEG, JPEG2000, MPEG
  • Sampling, Resampling, Signal reconstruction, Texture filtering
  • Z-transform, Recursive filtering
  • Deconvolution
  • Edge detection (Canny, Deriche, etc.)
  • Image descriptors (Haralick, Zernike, SIFT, MPEG-7)
  • Steerable filters
    recommended literature
  • GONZALEZ, Rafael C. and Richard E. WOODS. Digital image processing [2nd ed.]. 2nd ed. Upper Saddle River: Prentice Hall, 2002. xx, 793 s. ISBN 0-201-18075-8. info
  • BRACEWELL, Ronald N. The Fourier transform and its applications. 3rd ed. Boston: McGraw Hill, 2000. xx, 616. ISBN 0073039381. info
Teaching methods
obtaining knowledge during lectures, obtaining skills by working with PC
Assessment methods
During the semester, the students are required to solve the selected team project. The final defense of this project takes place during the last week of the semester. The students must successfully pass this defense in order to be allowed to take the final exam. The final exam consists of written and oral form. The written part contains questions that verify the students' skills and experience in the given field of image processing. The oral part follows the written part. Here, the students have a chance to explain or finalize those solutions from the written part that are incomplete.
Language of instruction
Further Comments
The course is taught annually.
The course is taught: every week.
Teacher's information
The course is also listed under the following terms Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Autumn 2019.
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