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Proceedings Paper

Phase retrieval via incremental reweighted gradient descent
Author(s): Shichao Cheng; Quanbing Zhang; Feihang Hu; Yufan Yuan
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Paper Abstract

In this paper, a phase retrieval algorithm based on the Incremental Truncation Wirtinger flow (ITWF) and reweighted gradient descent algorithm, which is called Incremental Reweighted Gradient Descent (IRGD). The presented IRGD algorithm is divided into two steps as most optimization algorithms: an initial estimation and an iterative refinement. In the iterative process of the algorithm, we refine the initial estimate value by combining the incremental with the reweighted gradient descent. Compared with WF and other algorithms which needs to pass through the entire data at each time, it has obvious advantages when dealing with large-scale signals. In order to speed up the convergence of iterative estimates and increase the robustness, we use the reweighted method to attach large weights to the reliable gradients and small weights to the spurious ones, and integrate the smoothing function and the relaxation parameter into the gradient descent formula. The simulation experimental results show that it can recover the unknown signal accurately under the given random Gaussian measurement with certain noise, and is superior to the most existing algorithms in convergence speed and success rate under the same condition.

Paper Details

Date Published: 12 December 2018
PDF: 6 pages
Proc. SPIE 10845, Three-Dimensional Image Acquisition and Display Technology and Applications, 108450O (12 December 2018); doi: 10.1117/12.2505467
Show Author Affiliations
Shichao Cheng, Anhui Univ. (China)
Quanbing Zhang, Anhui Univ. (China)
Feihang Hu, Anhui Univ. (China)
Yufan Yuan, Anhui Univ. (China)

Published in SPIE Proceedings Vol. 10845:
Three-Dimensional Image Acquisition and Display Technology and Applications
Byoungho Lee; Yongtian Wang; Liangcai Cao; Guohai Situ, Editor(s)

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