Fundamental image processing theories and algorithms. Signal representation using transforms, wavelets and frames is overviewed. Signal reconstruction methods using total variation, sparse coding and low-rank prior, based on convex optimization, are discussed. Applications include image compression, restoration and enhancement.
Prior background in digital signal processing (Electrical Engineering & Computer Science 4452 3.0 or equivalent) and numerical linear algebra is strongly recommended.
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