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HLLoApp is presented, a reconstruction tool that converts raw lensless holograms into analysis-ready amplitude and phase images through an integrated pipeline comprising robust autofocusing, shift/scale registration, and iterative complex-field reconstruction, with optional GPU acceleration for batch processing.
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Abstract Lensless holographic microscopy provides large-field, label-free amplitude and quantitative phase images that are attractive for high-throughput technical and biomedical analysis, yet routine use is often hindered by fragmented reconstruction workflows requiring manual intervention and cumbersome refocusing, registration, and iterative phase recovery. We present HoLLoApp, a reconstruction tool that converts raw lensless holograms into analysis-ready amplitude and phase images through an integrated pipeline comprising robust autofocusing, shift/scale registration, and iterative complex-field reconstruction, with optional GPU acceleration for batch processing. We benchmark HoLLoApp on representative datasets and assessing reconstruction fidelity under acquisition variability, together with computational performance and throughput. HoLLoApp exports reconstructed images alongside the full set of processing parameters and metadata, enabling reproducible reanalysis and standardized comparisons across experiments. By consolidating key reconstruction steps into a single, configurable workflow, HoLLoApp lowers the practical barrier to scalable, label-free, lensless holographic complex-field imaging and supports downstream quantitative bioimage analysis reconstruction framework.
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@article{Grski2026HoLLoApp,
title = {HoLLoApp: a reconstruction tool for high-throughput label-free complex-field imaging in lensless holographic microscopy},
author = {Bartosz Górski and Mikołaj Rogalski and Emilia Wdowiak and Piotr Arcab and Kamil Kalinowski and Maciej Trusiak},
journal = {Journal of Physics Photonics},
year = {2026},
doi = {10.1088/2515-7647/ae8b49},
url = {https://doi.org/10.1088/2515-7647/ae8b49}
}
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