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Non-invasive imaging through strongly scattering media based on speckle pattern estimation and deconvolution

机译:基于散斑图估计和反卷积的强散射介质非侵入性成像

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摘要

Imaging through scattering media is still a formidable challenge with widespread applications ranging from biomedical imaging to remote sensing. Recent research progresses provide several feasible solutions, which are hampered by limited complexity of targets, invasiveness of data collection process and lack of robustness for reconstruction. In this paper, we show that the complex to-be-observed targets can be non-invasively reconstructed with fine details. Training targets, which can be directly reconstructed by speckle correlation and phase retrieval, are utilized as the input of the proposed speckle pattern estimation model, in which speckle modeling and constrained least square optimization are applied to estimate the distribution of the speckle pattern. Reconstructions for to-be-observed targets are realized by deconvoluting the estimated speckle pattern from the acquired integrated intensity matrices (IIMs). The qualities of reconstructed results are ensured by the stable statistical property and memory effect of laser speckle patterns. Experimental results show that the proposed method can reconstruct complex targets in high quality and the reconstruction performance is robust even much less data are acquired.
机译:通过散射介质成像在生物医学成像到遥感等广泛应用中仍然是一个巨大的挑战。最近的研究进展提供了几种可行的解决方案,这些解决方案由于目标的复杂性有限,数据收集过程的侵入性以及重建的鲁棒性而受到阻碍。在本文中,我们表明可以通过精细的细节无创地重建复杂的待观察目标。可以通过散斑相关性和相位检索直接重建的训练目标被用作提出的散斑图案估计模型的输入,其中散斑建模和约束最小二乘优化用于估计散斑图案的分布。通过从获得的积分强度矩阵(IIM)中对估计的斑点图案进行反卷积,可以实现对待观察目标的重建。稳定的统计特性和激光散斑图案的记忆效果可确保重建结果的质量。实验结果表明,所提出的方法可以高质量地重建复杂目标,并且即使获取的数据少得多,重建性能也很鲁棒。

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