A phase retrieval algorithm based on total variation regularization

被引：0

作者：

Lian Q.-S. ^{[1
]}

Wei T.-J. ^{[1
]}

Chen S.-Z. ^{[1
]}

Shi B.-S. ^{[1
]}

机构：

[1] School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, Hebei

来源：

Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2017年 / 45卷 / 01期

关键词：

Gradient operator; Nonlinear compressive sensing; Phase retrieval; Sparsity; Total variation;

D O I：

10.3969/j.issn.0372-2112.2017.01.008

中图分类号：

学科分类号：

摘要：

The problem of phase retrieval, namely, recovery of a signal only from the magnitude of its Fourier transform, or of any other linear transform. Due to the loss of phase information, this problem is ill-posed. Therefore, the prior knowledge is required to enable its accurate reconstruction. In this work, based on the framework of nonlinear compressive sensing, a novel phase retrieval algorithm which exploits the sparsity of the natural images under the image gradient operator is proposed. The algorithm incorporates the total variation regularization into the phase retrieval problem, which based on support constraints and amplitude constraints. Moreover, alternating direction method of multipliers (ADMM) is utilized for solving the corresponding non-convex optimization problem. Experimental results indicate that the performance of the proposed algorithm outperforms the classical algorithms, such as HIO, RAAR, moreover, it is robust to noise. © 2017, Chinese Institute of Electronics. All right reserved.

引用

页码：54 / 60

页数：6

共 28 条

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