TY - JOUR
T1 - An alternating optimization approach for phase retrieval
AU - Ming, Huaiping
AU - Huang, Dongyan
AU - Xie, Lei
AU - Li, Haizhou
AU - Dong, Minghui
N1 - Publisher Copyright:
Copyright © 2015 ISCA.
PY - 2015
Y1 - 2015
N2 - In this paper, we address the problem of phase retrieval to re- cover a signal from the magnitude of its Fourier transform. In many applications of phase retrieval, the signals encountered are naturally sparse. In this work, we consider the case where the signal is sparse under the assumption that few components are nonzero. We exploit further the sparse nature of the signal- s and propose a two stage sparse phase retrieval algorithm. A simple iterative minimization algorithm recovers a sparse sig- nal from measurements of its Fourier transform (or other lin- ear transform) magnitude based on the minimization of a block l1 norm. We show in the experiments that the proposed algorithm achieves a competitive performance. It is robust to noise and scalable in practical implementation. The proposed method converges to a more accurate and stable solution than other ex- isting techniques for synthetic signals. For speech signals, ex- periments show that the voice quality of reconstructed speech signals is almost as good as the original signals.
AB - In this paper, we address the problem of phase retrieval to re- cover a signal from the magnitude of its Fourier transform. In many applications of phase retrieval, the signals encountered are naturally sparse. In this work, we consider the case where the signal is sparse under the assumption that few components are nonzero. We exploit further the sparse nature of the signal- s and propose a two stage sparse phase retrieval algorithm. A simple iterative minimization algorithm recovers a sparse sig- nal from measurements of its Fourier transform (or other lin- ear transform) magnitude based on the minimization of a block l1 norm. We show in the experiments that the proposed algorithm achieves a competitive performance. It is robust to noise and scalable in practical implementation. The proposed method converges to a more accurate and stable solution than other ex- isting techniques for synthetic signals. For speech signals, ex- periments show that the voice quality of reconstructed speech signals is almost as good as the original signals.
KW - Damped gauss-Newton method
KW - Phase retrieval
KW - Sparse coding
UR - http://www.scopus.com/inward/record.url?scp=84959174209&partnerID=8YFLogxK
M3 - 会议文章
AN - SCOPUS:84959174209
SN - 2308-457X
VL - 2015-January
SP - 3426
EP - 3430
JO - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
JF - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
T2 - 16th Annual Conference of the International Speech Communication Association, INTERSPEECH 2015
Y2 - 6 September 2015 through 10 September 2015
ER -