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A fast-converging iterative method based on weighted feedback for multi-distance phase retrieval

机译:基于加权反馈的多距离相位检索快速收敛迭代方法

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摘要

Multiple distance phase retrieval methods hold great promise for imaging and measurement due to their less expensive and compact setup. As one of their implementations, the amplitude-phase retrieval algorithm (APR) can achieve stable and high-accuracy reconstruction. However, it suffers from the slow convergence and the stagnant issue. Here we propose an iterative modality named as weighted feedback to solve this problem. With the plug-ins of single and double feedback, two augmented approaches, i.e. the APRSF and APRDF algorithms, are demonstrated to increase the convergence speed with a factor of two and three in experiments. Furthermore, the APRDF algorithm can extend the multiple distance phase retrieval to the partially coherent illumination and enhance the imaging contrast of both amplitude and phase, which actually relaxes the light source requirement. Thus the weighted feedback enables a fast-converging and high-contrast imaging scheme for the iterative phase retrieval.
机译:多距离相位检索方法因其价格便宜且结构紧凑而具有广阔的成像和测量前景。作为其实现之一,幅度相位检索算法(APR)可以实现稳定且高精度的重构。但是,它受到收敛速度慢和问题停滞的困扰。在这里,我们提出了一种称为加权反馈的迭代方式来解决此问题。在单反馈和双反馈的插件中,两种增强方法(即APRSF和APRDF算法)在实验中被证明可以将收敛速度提高2到3倍。此外,APRDF算法可以将多距离相位检索扩展到部分相干照明,并增强幅度和相位的成像对比度,这实际上放宽了对光源的要求。因此,加权反馈实现了用于迭代相位检索的快速收敛和高对比度成像方案。

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