Unified framework and algorithm for quantized compressed sensing

Details Unified framework and algorithm for quantized compressed sensing Unified framework and algorithm for quantized compressed sensing

2012-03-22

arxiv.org/abs/1203.4870v1

Computer Science

Information Theory

20 pages, 6 figures

Scientific paper

Compressed sensing (CS) studies the recovery of high dimensional signals from their low dimensional linear measurements under a sparsity prior. This paper is focused on the CS problem with quantized measurements. There have been research results dealing with different scenarios including a single/multiple bits per measurement, noiseless/noisy environment, and an unsaturated/saturated quantizer. While the existing methods are only for one or more specific cases, this paper presents a framework to unify all the above mentioned scenarios of the quantized CS problem. Under the unified framework, a variational Bayesian inference based algorithm is proposed which is applicable to the signal recovery of different application cases. Numerical simulations are carried out to illustrate the improved signal recovery accuracy of the unified algorithm in comparison with state-of-the-art methods for both multiple and single bit CS problems.

Affiliated with

Also associated with

No associations

Rating

Unified framework and algorithm for quantized compressed sensing does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Unified framework and algorithm for quantized compressed sensing, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Unified framework and algorithm for quantized compressed sensing will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-381211