Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, China.
Sci Rep. 2018 Jun 14;8(1):9088. doi: 10.1038/s41598-018-27467-1.
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.
通过散射介质进行成像是一个具有挑战性的问题,其应用广泛,包括生物医学成像和遥感。最近的研究进展提供了几种可行的解决方案,但这些方案受到目标复杂性有限、数据采集过程的侵入性以及重建的稳健性缺乏的限制。在本文中,我们表明可以非侵入性地重建具有精细细节的复杂待观察目标。利用可以直接通过散斑相关和相位恢复重建的训练目标作为所提出的散斑模式估计模型的输入,在该模型中,应用散斑建模和约束最小二乘优化来估计散斑模式的分布。通过从所获取的积分强度矩阵 (IIM) 中解卷积估计的散斑模式来实现对观测目标的重建。重建结果的质量通过激光散斑模式的稳定统计特性和记忆效应来保证。实验结果表明,所提出的方法可以高质量地重建复杂目标,并且即使采集的数据较少,重建性能也很稳健。