For a detailed course outline follow [.html][.pdf]
This course reviews the application of wavelets and related transforms (curvelets / contourlets & surfacelets)) to signal/image processing, inverse problems and compressive sampling with applications to (seismic) imaging, source separation, sub-Nyquist sampling and geophysical signal processing and inverse theory. These techniques are important for areas in Science and Engineering where data is incomplete, noisy and contains edges that carry the important information. It is shown how this data can be recovered by nonlinear programs that promote transform-based sparsity.
Approximately 50 % of the course consists of lectures. The remainder consists of course projects and the in-class discussion of papers from the current literature. As part of the discussions and projects, students are encouraged to use (and test) existing software packages that include WaveLab, SparseLab, CurveLab, Sparco, and SPGl1. Students will be evaluate by their discussion of the current literature and by their project presentation and project paper.