Lectures:
1. The space of image signals. Linear combination and the dot product. The basis in the space of image signals.
2. The operator. Linear and shift invariant operators. Dirac delta function.
3. Convolution. Discrete convolution. Applications of convolution.
4. The Fourier transform, its importance and properties.
5. Discrete Fourier transform, cosine transform, fast Fourier transform, wavelet transform.
6. Applications of the Fourier transform in image processing. Modifying the frequency spectrum of images.
7. JPEG compression, MPEG compression, H.264/H.265 compression method, audio compression.
8. Sampling and reconstructing images. Aliasing. Quantization.
9. Transformations of brightness. Gamma correction. Histogram equalization.
10. Geometric transformations of images. Morphing and warping.
11. Recursive and non-recursive image filtering. Inverse filter.
12. Random fields and their applications in image processing. Wiener filter.
13. Morphological image processing.
14. Technical equipment for capturing and processing images, cameras and camera systems.
Computer labs:
1. Introduction to the OpenCV library, and basic operations with images.
2. Gamma correction and contrast enhancement.
3. Convolution (with Gaussian, Laplace, and other masks).
4. Anisotropic filtering of images.
5. Forward and backward discrete Fourier transform.
6. Wavelet transform.
7. Filtering in the frequency domain.
8. Geometric transformations of images.
9. Removing the lens distortion.
10. Histogram equalization.
11. Projective image transformation.
12. Morphological operations on binary images.
13. Spare space.
14. Credit.
1. The space of image signals. Linear combination and the dot product. The basis in the space of image signals.
2. The operator. Linear and shift invariant operators. Dirac delta function.
3. Convolution. Discrete convolution. Applications of convolution.
4. The Fourier transform, its importance and properties.
5. Discrete Fourier transform, cosine transform, fast Fourier transform, wavelet transform.
6. Applications of the Fourier transform in image processing. Modifying the frequency spectrum of images.
7. JPEG compression, MPEG compression, H.264/H.265 compression method, audio compression.
8. Sampling and reconstructing images. Aliasing. Quantization.
9. Transformations of brightness. Gamma correction. Histogram equalization.
10. Geometric transformations of images. Morphing and warping.
11. Recursive and non-recursive image filtering. Inverse filter.
12. Random fields and their applications in image processing. Wiener filter.
13. Morphological image processing.
14. Technical equipment for capturing and processing images, cameras and camera systems.
Computer labs:
1. Introduction to the OpenCV library, and basic operations with images.
2. Gamma correction and contrast enhancement.
3. Convolution (with Gaussian, Laplace, and other masks).
4. Anisotropic filtering of images.
5. Forward and backward discrete Fourier transform.
6. Wavelet transform.
7. Filtering in the frequency domain.
8. Geometric transformations of images.
9. Removing the lens distortion.
10. Histogram equalization.
11. Projective image transformation.
12. Morphological operations on binary images.
13. Spare space.
14. Credit.