Lecture 1: Multidimensional signals and systems
Notation, Multidimensional signals and systems, Fourier transform
Lecture Notes
Lecture 2: Sampling and Interpolation
Dirac’s delta, Sampling Theorem, Interpolation
Lecture 3: Discrete-domain signals and systems
Discrete-domain Fourier transform, Sampling, Aliasing, Interpolation, Polyphase representation
Lecture 4: Random signals
Elementary probabilty, Random signals
Lecture 5: Statistical estimation
Inverse problems, statistical estimation
Lecture 6: Patch-based priors
Patch-based denoising, NLM, Bilateral filter
Lecture 7: Sparsity-based priors
Patch-based denoising, NLM, Bilateral filter