Phase retrieval via incremental reweighted gradient descent

Shichao Cheng; Quanbing Zhang; Feihang Hu; Yufan Yuan

doi:10.1117/12.2505467

12 December 2018 Phase retrieval via incremental reweighted gradient descent

Shichao Cheng, Quanbing Zhang, Feihang Hu, Yufan Yuan

Author Affiliations +

Proceedings Volume 10845, Three-Dimensional Image Acquisition and Display Technology and Applications; 108450O (2018) https://doi.org/10.1117/12.2505467
Event: International Symposium on Optoelectronic Technology and Application 2018, 2018, Beijing, China

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.

Citation Download Citation

Shichao Cheng, Quanbing Zhang, Feihang Hu, and Yufan Yuan "Phase retrieval via incremental reweighted gradient descent", Proc. SPIE 10845, Three-Dimensional Image Acquisition and Display Technology and Applications, 108450O (12 December 2018); https://doi.org/10.1117/12.2505467

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