Compressive optical interferometry by Davood Mardani, H. Esat Kondakci, Lane Martin, Ayman F. Abouraddy, George K. Atia
Compressive sensing (CS) combines data acquisition with compression coding to reduce the number of measurements required to reconstruct a sparse signal. In optics, this usually takes the form of projecting the field onto sequences of random spatial patterns that are selected from an appropriate random ensemble. We show here that CS can be exploited in `native' optics hardware without introducing added components. Specifically, we show that random sub-Nyquist sampling of an interferogram helps reconstruct the field modal structure. The distribution of reduced sensing matrices corresponding to random measurements is provably incoherent and isotropic, which helps us carry out CS successfully.
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.
Maybe subrandom sampling can help with compressive sensing, perhaps being better than purely random sampling:
ReplyDeletehttps://en.wikipedia.org/wiki/Low-discrepancy_sequence
I suppose there is a good chance it has already been investigated.