Robust Phase Retrieval with Outliers

Xue Jiang, H. So, Xingzhao Liu
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引用次数: 1

Abstract

An outlier-resistance phase retrieval algorithm based on alternating direction method of multipliers (ADMM) is devised in this paper. Instead of the widely used least squares criterion that is only optimal for Gaussian noise environment, we adopt the least absolute deviation criterion to enhance the robustness against outliers. Considering both intensityand amplitude-based observation models, the framework of ADMM is developed to solve the resulting non-differentiable optimization problems. It is demonstrated that the core subproblem of ADMM is the proximity operator of the ℓ1-norm, which can be computed efficiently by soft-thresholding in each iteration. Simulation results are provided to validate the accuracy and efficiency of the proposed approach compared to the existing schemes.
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基于异常值的鲁棒相位检索
提出了一种基于乘法器交替方向法(ADMM)的离群电阻相位恢复算法。我们采用最小绝对偏差准则来提高对异常值的鲁棒性,而不是广泛使用的仅对高斯噪声环境最优的最小二乘准则。同时考虑基于强度和振幅的观测模型,开发了ADMM的框架来解决由此产生的不可微优化问题。证明了ADMM的核心子问题是1-范数的接近算子,在每次迭代中采用软阈值法可以有效地计算出接近算子。仿真结果验证了该方法与现有方案的准确性和有效性。
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