| Course Unit Code | 460-4079/02 |
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| Number of ECTS Credits Allocated | 4 ECTS credits |
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| Type of Course Unit * | Optional |
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| Level of Course Unit * | Second Cycle |
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| Year of Study * | |
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| Semester when the Course Unit is delivered | Winter Semester |
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| Mode of Delivery | Face-to-face |
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| Language of Instruction | English |
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| Prerequisites and Co-Requisites | Course succeeds to compulsory courses of previous semester |
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| Name of Lecturer(s) | Personal ID | Name |
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| SOJ10 | doc. Dr. Ing. Eduard Sojka |
| Summary |
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The following topics are covered: Mathematical preliminaries for digital image processing, Fourier, cosine, and wavelet transforms and their applications, JPEG, MPEG, and audio compression, point and geometric operations, sampling and reconstructing images, filtering, stochastic approach to digital image processing, morphological image processing, image enhancement using deep learning. The course includes the computer labs in which the computer programs are realised corresponding to the mentioned topics.
Graduates of this course will be able to:
- Describe and explain the theoretical foundations of operations on image signal space, including Fourier, cosine, and wavelet transforms, convolution, and filtering.
- Describe and explain the principles of image and video compression.
- Apply theoretical knowledge in the creation of algorithms that solve the problem of various image modifications.
- Evaluate, discuss, select, and compare the quality and effectiveness of various algorithms for working with images. |
| Learning Outcomes of the Course Unit |
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The course acquaints the students with the foundations of digital image processing. After passing the course, the student will understand the principles of the operations with the images and will be able to implement them.
Graduates of this course will be able to:
- Describe and explain the theoretical foundations of operations on image signal space, including Fourier, cosine, and wavelet transforms, convolution, and filtering.
- Describe and explain the principles of image and video compression.
- Apply theoretical knowledge in the creation of algorithms that solve the problem of various image modifications.
- Evaluate, discuss, select, and compare the quality and effectiveness of various algorithms for working with images.
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| Course Contents |
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Lectures:
- The space of image signals. Linear combination and the dot product. The basis in the space of image signals.
- The operator. Linear and shift invariant operators. Dirac delta function.
- Convolution. Discrete convolution. Applications of convolution.
- The Fourier transform, its importance and properties.
- Discrete Fourier transform, cosine transform, fast Fourier transform, wavelet transform.
- Applications of the Fourier transform in image processing. Modifying the frequency spectrum of images.
- JPEG compression, MPEG compression, H.264/H.265 compression method, audio compression.
- Sampling and reconstructing images. Aliasing. Quantization.
- Transformations of brightness. Gamma correction. Histogram equalization.
- Geometric transformations of images. Morphing and warping.
- Recursive and non-recursive image filtering. Inverse filter.
- Random fields and their applications in image processing. Wiener filter.
- Morphological image processing.
- Image enhancement using deep learning.
- Technical equipment for capturing and processing images, cameras and camera systems.
Computer labs:
- Introduction to the OpenCV library, and basic operations with images.
- Gamma correction and contrast enhancement.
- Convolution (with Gaussian, Laplace, and other masks).
- Anisotropic filtering of images.
- Forward and backward discrete Fourier transform.
- Wavelet transform.
- Filtering in the frequency domain.
- Geometric transformations of images.
- Removing the lens distortion.
- Histogram equalization.
- Projective image transformation.
- Morphological operations on binary images.
- Image enhancement using deep learning.
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| Recommended or Required Reading |
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| Required Reading: |
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1. Gonzalez, R., C., Woods, R., E.: Digital Image Processing, 4th Edition, Pearson, ISBN-13: 9780134734804, 9780133356724, 2018.
2. Shih, Frank, Y.: AI Deep Learning in Image Processing, CRC Press, ISBN 9781032755304, 2025.
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1. Sojka, E., Gaura, J., Krumnikl, M.: Matematické základy digitálního zpracování obrazu, VŠB-TU Ostrava, 2011.
2. Sojka, E.: Digitální zpracování a analýza obrazů, učební texty, VŠB-TU Ostrava, 2000 (ISBN 80-7078-746-5).
3. Gonzalez, R., C., Woods, R., E.: Digital Image Processing, 4th Edition, Pearson, ISBN-13: 9780134734804, 9780133356724, 2018.
4. Shih, Frank, Y.: AI Deep Learning in Image Processing, CRC Press, ISBN 9781032755304, 2025.
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| Recommended Reading: |
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1. Burger, W., Burge, M., J.: Principles of Digital Image Processing: Fundamental Techniques, Springer, ISBN-10: 1848001908, ISBN-13: 978-1848001909, 2011
2. Brahmbhatt, S.: Practical OpenCV (Technology in Action), Apress, ISBN-10: 1430260793, ISBN-13: 978-1430260790, 2013
3. Petrou, M., Petrou, C.: Image Processing: The Fundamentals, 2nd Edition, Wiley, ISBN 978-1-119-99439-8, 2011 |
1. Burger, W., Burge, M., J.: Principles of Digital Image Processing: Fundamental Techniques, Springer, ISBN-10: 1848001908, ISBN-13: 978-1848001909, 2011
2. Brahmbhatt, S.: Practical OpenCV (Technology in Action), Apress, ISBN-10: 1430260793, ISBN-13: 978-1430260790, 2013
3. Petrou, M., Petrou, C.: Image Processing: The Fundamentals, 2nd Edition, Wiley, ISBN 978-1-119-99439-8, 2011 |
| Planned learning activities and teaching methods |
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| Lectures, Tutorials |
| Assesment methods and criteria |
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| Tasks are not Defined |