Image Filtering: Basics and Techniques

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Explores the convolution theorem, DTFT reconstruction, frequency response effects, and signal building.

Explores the Fourier transform, convolution product, and their applications in signal processing.

Explores digital filter synthesis, emphasizing frequency response approximation and pole-zero placement.

Covers digital RII filters, elliptical and Chebyshev filters, frequency transformations, and filter realization.

Explores filter design, boundary conditions, Fourier-domain filtering, and useful smoothing filters for image processing.

Covers Chebyshev polynomials, all-pole filter design, RLC filters, and active-RC and OTA-C filter implementations.

Explores the convolution of tempered distributions and solving differential equations using fundamental solutions.

Explores linear estimation, Wiener filters, and optimal prediction in signal processing.

Explores image restoration and signal prediction using linear filters in signal processing.

Covers parametric signal models and practical Matlab applications for Markov chains and AutoRegressive processes.