Mr. Zan Cimperman Profile

Zan Cimperman

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Proceedings Article | 20 May 2022 Poster + Paper

Autofocus algorithms for lensless on-chip microscopy validated on synthetic targets for microfluidic applications and particle tracking

Zan Cimperman, Peter Naglic, Franjo Pernuš, Boštjan Likar, Miran Bürmen

Proceedings Volume 12136, 1213617 (2022) https://doi.org/10.1117/12.2621693

KEYWORDS: 3D image reconstruction, Particles, Mie scattering, Microfluidics, Reconstruction algorithms, Microscopy, Optical simulations, Holograms

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Lensless on-chip microscopy comprises a simple and compact setup in which the sample is placed close to the imaging sensor and illuminated by a coherent light source. The acquired in-line hologram carries information about the amplitude and phase image of the sample, which can be numerically reconstructed. Contrary to conventional microscopy, the reconstructed images can be numerically refocused at desired focus planes effectively providing three-dimensional information. For reliable object reconstruction, a proper focus plane must be selected, which can be done automatically using an autofocusing algorithm. The autofocusing algorithms are commonly evaluated on synthetic or experimentally acquired in-line holograms. First are usually simulated with the same numerical propagation method as used for reconstruction and are not able to simulate holograms of truly three-dimensional objects, while experimentally acquired holograms can be affected by measurement noise and model mismatch artefacts. In this paper, we propose an objective evaluation of autofocusing algorithms on in-line holograms simulated by Mie theory and T-matrix method which can simulate holograms of truly three-dimensional spherical objects distributed in various spatial positions. We evaluate and compare different autofocusing algorithms in terms of the accuracy of the estimated focus plane and computational efficiency. Finally, we present a proof-of-concept real-time implementation of the autofocusing algorithm based on the open-source PyOpenCL framework. We found that the implemented autofocusing algorithms provided the best average accuracy of 1.75 μm and required 330 μs per evaluation cycle resulting in around 20 frames per second for autofocusing a 1024×1024 hologram.

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