HoLLoApp: a reconstruction tool for high-throughput label-free complex-field imaging in lensless holographic microscopy

Abstract Lensless holographic microscopy can produce large-field, label-free amplitude and quantitative phase measurements that are valuable for high-throughput technical and biomedical image analysis, but practical adoption is often limited by multi-step reconstruction workflows that require manual focusing, alignment, and iterative phase recovery—creating bottlenecks in throughput and reproducibility. We present HoLLoApp, an end-to-end reconstruction framework that converts raw holograms into analysis-ready amplitude and phase outputs through integrated modules for robust autofocusing, shift/scale correction, and iterative reconstruction, with optional GPU acceleration for large datasets. We benchmark HoLLoApp on representative holographic data, assessing reconstruction fidelity, robustness to acquisition variability, and computational efficiency, and demonstrate that the unified workflow enables consistent reconstructions suitable for downstream quantitative tasks, with relevance to bioimaging. HoLLoApp exports reconstructed images together with processing parameters and metadata to support reproducible reanalysis and batch processing across experiments. By standardizing and accelerating lensless hologram reconstruction, HoLLoApp provides a practical bridge from raw holographic measurements to scalable, label-free, high-throughput quantitative bioimage analysis reconstruction framework.