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.
- 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.