In it we explain how recent progress in the theory of compressed sensing allows one to significantly enhance the performance of sparse recovery techniques in imaging applications.
My former undergraduate student Anyi (Casie) Bao was a winner of the SFU Department of Mathematics Undergraduate Research Prize. Her work involved the development and analysis of compressed sensing-based strategies for correcting for corrupted measurements in Uncertainty Quantification. A draft version of the resulting paper can be found here:
Matt King-Roskamp – an undergraduate student in my group co-supervised with Simone Brugiapaglia – was awarded runner-up in two poster competitions this summer:
- The SFU Science Undergraduate Research Journal (SURJ) poster competition
- The SFU Symposium on Mathematics & Computation
His work, entitled Optimal Sampling Strategies for Compressive Imaging, presents new, theoretically optimal sampling techniques for imaging using compressed sensing.