Li, Q., Huang, L., Zhang, P. et al. (2 more authors) (2018) A fast gradient-based iterative algorithm for undersampled phase retrieval. IEEE Transactions on Aerospace and Electronic Systems, 54 (4). pp. 2086-2090. ISSN 0018-9251
Abstract
This letter develops a fast iterative shrinkage-thresholding algorithm, which can efficiently tackle the issue in undersampled phase retrieval. First, using the gradient framework and proximal regularization theory, the undersampled phase retrieval problem is formulated as an optimization in terms of least-absolute-shrinkage-and-selection-operator form with (ℓ2+ℓ1) -norm minimization in the case of sparse signals. A gradient-based phase retrieval via majorization–minimization technique (G-PRIME) is applied to solve a quadratic approximation of the original problem, which, however, suffers a slow convergence rate. Then, an extension of the G-PRIME algorithm is derived to further accelerate the convergence rate, in which an additional iteration is chosen with a marginal increase in computational complexity. Experimental results show that the proposed algorithm outperforms the state-of-the-art approaches in terms of the convergence rate.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Majorization–minimization; phase retrieval; proximal regularization; sparse signal |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 23 Aug 2018 11:49 |
Last Modified: | 12 Apr 2024 15:37 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/TAES.2018.2832558 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:134923 |