11 September 2015 Deterministic compressed sensing and quantization
Author Affiliations +
Compressed Sensing (CS) is a sampling paradigm used for acquiring sparse or compressible signals from a seemingly incomplete set of measurements. In any practical application with our digitally driven technology, these "compressive measurements" are quantized and thus they do not have infinite precision. So far, the theory of quantization in CS has mainly focused on compressive sampling systems designed with random measurement matrices. In this note, we turn our attention to "deterministic compressed sensing". Specifically, we focus on quantization in CS with chirp sensing matrices and present quantization approaches and numerical experiments.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Arman Ahmadieh, Arman Ahmadieh, Özgur Yilmaz, Özgur Yilmaz, } "Deterministic compressed sensing and quantization", Proc. SPIE 9597, Wavelets and Sparsity XVI, 95970P (11 September 2015); doi: 10.1117/12.2189211; https://doi.org/10.1117/12.2189211


Random encoding of quantized finite frame expansions
Proceedings of SPIE (September 25 2013)
Recovery of quantized compressed sensing measurements
Proceedings of SPIE (March 11 2015)
Angle-preserving quantized phase embeddings
Proceedings of SPIE (September 25 2013)
Two algorithms for compressing noise like signals
Proceedings of SPIE (May 24 2005)
Deterministic matrices with the restricted isometry property
Proceedings of SPIE (September 27 2011)

Back to Top