TY - GEN
T1 - An undersampled phase retrieval algorithm via gradient iteration
AU - Li, Qiang
AU - Liu, Wei
AU - Huang, Lei
AU - Sun, Weize
AU - Zhang, Peichang
N1 - Publisher Copyright:
©2018 IEEE.
PY - 2018/8
Y1 - 2018/8
N2 - This work addresses the issue of undersampled phase retrieval using the gradient framework and proximal regularization theorem.It is formulated as an optimization problem in terms of least absolute shrinkage and selection operator (LASSO) form with $(l-{2}+P-{1})$ norms minimization in the case of sparse incident signals.Then,inspired by the compressive phase retrieval via majorization-minimization technique (C-PRIME) algorithm, a gradient-based PRIME algorithm is proposed to solve a quadratic approximation of the original problem. Moreover, we also proved that the C-PRIME method can be regarded as a special case of the proposed algorithm. As demonstrated by simulation results,both the magnitude and phase recovery abilities of the proposed algorithm are excellent. Furthermore, the experimental results also show the mean square error (MSE) performance of the proposed algorithm versus iterative step.
AB - This work addresses the issue of undersampled phase retrieval using the gradient framework and proximal regularization theorem.It is formulated as an optimization problem in terms of least absolute shrinkage and selection operator (LASSO) form with $(l-{2}+P-{1})$ norms minimization in the case of sparse incident signals.Then,inspired by the compressive phase retrieval via majorization-minimization technique (C-PRIME) algorithm, a gradient-based PRIME algorithm is proposed to solve a quadratic approximation of the original problem. Moreover, we also proved that the C-PRIME method can be regarded as a special case of the proposed algorithm. As demonstrated by simulation results,both the magnitude and phase recovery abilities of the proposed algorithm are excellent. Furthermore, the experimental results also show the mean square error (MSE) performance of the proposed algorithm versus iterative step.
KW - Gradient iteration
KW - Majorization-minimization
KW - Sparse signal
KW - Undersampled phase retrieval
UR - http://www.scopus.com/inward/record.url?scp=85053633786&partnerID=8YFLogxK
U2 - 10.1109/SAM.2018.8449000
DO - 10.1109/SAM.2018.8449000
M3 - Conference article published in proceeding or book
AN - SCOPUS:85053633786
SN - 9781538647523
T3 - Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop
SP - 228
EP - 231
BT - 2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop, SAM 2018
PB - IEEE Computer Society
T2 - 10th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2018
Y2 - 8 July 2018 through 11 July 2018
ER -