Thursday, November 01, 2012

CPRL – An Extension of Compressive Sensing to the Phase Retrieval Problem - implementation -

We covered it last year ( Compressive Phase Retrieval Implementation ) but it seems the new algorithm is now using ADMM. As mentioned in the last review, it looks like ADMM (see here for Matlab scripts for ADMM from S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein) is becoming indeed a mainstay. 



CPRL – An Extension of Compressive Sensing to the Phase Retrieval Problem by Henrik Ohlsson, Allen Y. Yang, Roy Dong, S. Shankar Sastry.. The abstract reads:

While compressive sensing (CS) has been one of the most vibrant research fields in the past few years, most development only applies to linear models. This limits its application in many areas where CS could make a difference. This paper presents a novel extension of CS to the phase retrieval problem, where intensity measurements of a linear system are used to recover a complex sparse signal. We propose a novel solution using a lifting technique – CPRL, which relaxes the NP-hard problem to a nonsmooth semidefinite program. Our analysis shows that CPRL inherits many desirable properties from CS, such as guarantees for exact recovery. We further provide scalable numerical solvers to accelerate its implementation.
An implementation is available here 



Join our Reddit Experiment, Join the CompressiveSensing subreddit and post there !
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.

No comments:

Printfriendly