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Polar optical flow network: extracting overlapped holograms of dynamic particles

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Abstract

Digital holography with lensless in-line setup has been extensively used in particle field measurements. As particle concentration increases, the holograms of dynamic particles locating at different depths tend to superpose with each other with incoherent overlap, hampering effective measurement of individual particles with incomplete information. Drawing inspiration from suborbicular nature of the in-line holographic fringes, in this study, we propose an optical flow method in polar coordinates to mitigate the overlap issue. The approach employs a radial transformer-enhanced network that leverages both the radial and angular characteristics of the polar hologram. Through ablation tests and experimental results, we have demonstrated the effectiveness and superiority of our proposed method.

© 2023 Optica Publishing Group

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Data availability

The data and codes of POF-net are available in Ref. [12].

12. IPC-Deeplearning, “POF-net,” GitHub (2023) [accessed 1 September 2023]https://github.com/ipc-deeplearning/POF-net.

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