Explores fundamental notions in image and video processing, covering applications and key concepts like color quantization and 2D Fourier Transform properties.
Explores elementary properties of Fourier Transforms, convolution, Parseval's Theorem, and the d'Alembert solution of the wave equation using Fourier Transforms and convolution.
Explores the Discrete Fourier Transform synthesis and analysis formulas, time shifts for finite-length signals, and the equivalence between DFS and DFT.
Covers the theory of numerical methods for frequency estimation on deterministic signals, including Fourier series and transform, Discrete Fourier transform, and the Sampling theorem.
Covers the Fourier transform, its properties, applications in signal processing, and differential equations, emphasizing the concept of derivatives becoming multiplications in the frequency domain.