Statistical Signal and Image Analysis

4 credits

A graduate level course on signal and image analysis spanning three main themes. Statistical signal processing: random processes, stochasticity, stationarity, Wiener filter, Kalman filter, matched filter; signal processing: time-frequency representations, wavelets, signal processing with sparse representation (dictionary learning); image processing: registration, image degradation and restoration: noise models + denoising, image pyramids, random fields. Prerequisites: DSC 210 or ECE 269 and DSC 212. Need to verify background in signal processing from undergraduate or other graduate courses. Restricted to DSC-MS-001 and DSC-PhD-001 students.