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通过同步多角度发光二极管照明实现单次无透镜成像。

Single-shot lensless imaging via simultaneous multi-angle LED illumination.

作者信息

Zhou You, Wu Jiamin, Suo Jinli, Han Xiaofei, Zheng Guoan, Dai Qionghai

出版信息

Opt Express. 2018 Aug 20;26(17):21418-21432. doi: 10.1364/OE.26.021418.

DOI:10.1364/OE.26.021418
PMID:30130850
Abstract

Lensless imaging is a technique that records diffraction patterns without using lenses and recovers the complex field of object via phase retrieval. Robust lensless phase retrieval process usually requires multiple measurements with defocus variation, transverse translation or angle-varied illumination. However, making such diverse measurements is time-consuming and limits the application of lensless setup for dynamic samples. In this paper, we propose a single-shot lensless imaging scheme via simultaneous multi-angle LED illumination. Diffraction patterns under multi-angle lights are recorded by different areas of the sensor within a single shot. An optimization algorithm is applied to utilize the single-shot measurement and retrieve the aliasing information for reconstruction. We first use numerical simulations to evaluate the proposed scheme quantitatively by comparisons with the multi-acquisition case. Then a proof-of-concept lensless setup is built to validate the method by imaging a resolution chart and biological samples, achieving ∼ 4.92 μm half-pitch resolution and ∼ 1.20mm field of view (FOV). We also discuss different design tradeoffs and present a 4-frame acquisition scheme (with ∼ 3.48 μm half-pitch resolution and ∼ 2.35 × 2.55 mm FOV) to show the flexibility of performance enhancement by capturing more measurements.

摘要

无透镜成像技术是一种无需使用透镜即可记录衍射图样,并通过相位恢复来重建物体复场的技术。稳健的无透镜相位恢复过程通常需要通过离焦变化、横向平移或角度变化照明进行多次测量。然而,进行如此多样的测量既耗时,又限制了无透镜装置在动态样本中的应用。在本文中,我们提出了一种通过同步多角度LED照明的单次无透镜成像方案。单次拍摄过程中,传感器的不同区域记录多角度光下的衍射图样。应用一种优化算法利用单次测量结果并检索用于重建的混叠信息。我们首先通过数值模拟,与多次采集的情况进行比较,对所提出的方案进行定量评估。然后搭建了一个概念验证无透镜装置,通过对分辨率测试图和生物样本成像来验证该方法,实现了约4.92μm的半间距分辨率和约1.20mm的视场(FOV)。我们还讨论了不同的设计权衡,并提出了一种4帧采集方案(半间距分辨率约为3.48μm,视场约为2.35×2.55mm),以展示通过采集更多测量数据来增强性能的灵活性。

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引用本文的文献

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Biomed Opt Express. 2023 Jan 3;14(2):489-532. doi: 10.1364/BOE.480685. eCollection 2023 Feb 1.
2
Integration of Fourier ptychography with machine learning: an alternative scheme.傅里叶叠层成像与机器学习的整合:一种替代方案。
Biomed Opt Express. 2022 Jul 21;13(8):4278-4297. doi: 10.1364/BOE.464001. eCollection 2022 Aug 1.