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Robust autofocusing method for multi-wavelength lensless imaging

delete2019-08-05
delete11
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OA
AI
刘坚 封面图
刘坚 (Jian Liu)
Y
Yixuan Zhao
C
Cheng Guo
W
Weisong Zhao
张
张雨田 (Yutian Zhang)
C
Changliang Guo
李
李浩宇 (Haoyu Li) *
DOI:10.1364/OE.27.023814delete
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摘要

摘要

En 中文
Lensless imaging based on multi-wavelength phase retrieval becomes a promising technology widely used as it has simple acquisition, miniaturized size and low-cost setup. However, measuring the sample-to-sensor distance with high accuracy, which is the key for high-resolution reconstruction, is still a challenge. In this work, we propose a multiwavelength criterion to realize autofocusing modulation, i.e., achieving much higher accuracy in determining the sample-to-sensor distance, compared to the conventional methods. Three beams in different spectrums are adopted to illuminate the sample, and the resulting holograms are recorded by a CCD camera. The patterns calculated by performing back propagation of the recorded holograms, with exhaustively searched sample-to-sensor distance value, are adopted to access the criterion. Image sharpness can be accessed and the optimal sample-to-sensor distance can be finely determined by targeting the valley of the curve given by the criterion. Through our novel multi-wavelength based autofocusing strategy and executing further phase retrieval process, high-resolution images can be finally retrieved. The applicability and robustness of our method is validated both in simulations and experiments. Our technique provides a useful tool for multi-wavelength lensless imaging under limited experimental conditions. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
Keyword:
DIGITAL HOLOGRAPHIC MICROSCOPY
PHASE-CONTRAST MICROSCOPY
NUMERICAL RECONSTRUCTION
FOCUS DETECTION
RETRIEVAL
CRITERION
FIELD
TRACKING
DIFFRACTION
ALGORITHM

期刊

Optics Express 封面图
Optics Express
IF:
3.3
论文数:
6.1W
被引数:
14.3W

机构

H
harbin institute of technology
学者数:
8.0W
论文数: 6.6W
被引数: 66
University of California System 封面图
University of California System
学者数:
37.7W
论文数: 33.8W
被引数: 6.6K
引用论文

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