Explore a concise yet comprehensive overview of compressed sensing and its applications in engineering applied mathematics. Gain insights into the context of sparsity and compression, learn practical rules of thumb, and discover key ingredients for applying compressed sensing effectively. Delve into topics such as compression versus compressed sensing, pixel space, reconstruction techniques, beating Shannon-Nyquist theorem, robust statistics, outlier rejection, L1 minimization, and the promotion of sparsity. Understand why compressed sensing is not limited to image processing and explore its broader applications in various fields.
Overview
Syllabus
A COMPRESSED OVERVIEW OF SPARSITY
Compression vs. Compressed Sensing
Pixel Space is (Larger Than) Astronomical
Reconstruction by Compressed Sensing
Beating Shannon-Nyquist
Robust Statistics and Outlier Rejection
A Compressed Summary of L1 Minimization
Why does L1 Minimization Promote Sparsity?
Ingredients of Compressed Sensing
Not Just Useful for Images
Taught by
Steve Brunton
