Research on the Wirtinger Flow algorithm based on quadratic distribution initial value

Zhenfei Xie, Xuelian Yu, Zhengxian Wang, Heng Li
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Abstract

Phase retrieval algorithms, such as the Wirtinger Flow (WF) algorithm, are widely used in various fields. As a nonconvex optimization algorithm for phase retrieval, WF is commonly employed in the reconstruction of holograms in holographic image projection. These types of algorithms typically involve two stages: an initialization stage and an iterative optimization stage. In the initialization stage, an initial value is provided, and a spectral method is used to calculate an approximate solution as the initial guess. The iterative optimization stage then utilizes the Wirtinger gradient to iteratively compute and converge the initial guess to a nearby real solution, thereby obtaining the global optimal solution. However, due to the random nature of the initial values, the computed results often exhibit significant instability. To address this issue, this paper proposes an approach based on a quadratic distribution for improving the stability of the results. In the initialization stage, the initial value is set as the quadratic distribution initial value. Then, the spectral method is applied again to calculate the initial guess. Since the quadratic distribution initial value is artificially assigned, it enhances the stability of the computed results. To validate this method, the paper applies the quadratic distribution initial value to both the initialization stage of the WF algorithm and the Truncated Amplitude Flow (TAF) algorithm. A comparison is made between the results obtained using random initial value and those obtained using the quadratic distribution initial values. The results demonstrate that compared to random initial values, the quadratic distribution initial values can achieve faster and equally accurate computation results with higher stability. Finally, this method is applied to simulation experiments of in-line digital holography, and the reconstruction results from the experiments further confirm the effectiveness of our approach.
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基于二次分布初值的 Wirtinger Flow 算法研究
相位检索算法,如 Wirtinger Flow(WF)算法,被广泛应用于各个领域。作为一种用于相位检索的非凸优化算法,WF 通常用于全息图像投影中的全息图重建。这类算法通常包括两个阶段:初始化阶段和迭代优化阶段。在初始化阶段,提供一个初始值,并使用光谱方法计算一个近似解作为初始猜测。然后,在迭代优化阶段,利用 Wirtinger 梯度进行迭代计算,将初始猜测值收敛到附近的真实解,从而获得全局最优解。然而,由于初始值的随机性,计算结果往往表现出明显的不稳定性。针对这一问题,本文提出了一种基于二次分布的方法,以提高结果的稳定性。在初始化阶段,初始值设定为二次分布初始值。然后,再次应用频谱法计算初始猜测。由于二次分布初始值是人为指定的,因此提高了计算结果的稳定性。为了验证这种方法,本文将二次分布初始值应用于 WF 算法和截断振幅流 (TAF) 算法的初始化阶段。比较了使用随机初始值和使用二次分布初始值得出的结果。结果表明,与随机初始值相比,二次分布初始值可以更快地获得同样精确的计算结果,而且稳定性更高。最后,将该方法应用于在线数字全息的模拟实验,实验的重建结果进一步证实了我们的方法的有效性。
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