arXiv subjects

Emilia Wdowiak

Publications and source records attributed to Emilia Wdowiak.

8 recordsLinked to original sources

arXiv2026

Hybrid spectral-spatial domain registration for nanometric tracking in digital in-line holographic microscopy

Digital in-line holographic microscopy enables label-free tracking and metrology, but achieving nanometric sub-pixel displacement accuracy over a wide capture range remains challenging. Frequency-domain registration based on the discrete Fourier transform (DFT) is globally stable and tolerant to large shifts, yet it suffers from sub-pixel quantization and interpolation artifacts that limit precision near zero displacement. In contrast, spatial-gradient refinement such as Lucas-Kanade (LK) can reach very high sub-pixel accuracy, but it is strongly initialization-limited and prone to divergence outside a narrow convergence basin. Here we propose a Hybrid Spectral-Spatial Domain (HSSD) framework that resolves this trade-off by combining the global robustness of a DFT-based coarse estimator with LK refinement of the residual motion. The DFT stage provides reliable initialization and substantially extends the capture range, while the LK stage suppresses the precision floor by reducing interpolation and quantization errors characteristic of standalone frequency-domain methods. We validate HSSD using numerical simulations and experiments in a transmission in-line holographic imaging system, achieving nanoprecision displacement measurement and stable tracking across a wide range of displacements and defocusing conditions. This hybrid strategy enables reliable nanometric localization in regimes where standalone DFT or LK methods either fail to converge or saturate in accuracy.

physics.optics↗

arXiv2026

Near-infrared lensless holographic microscopy on a visible sensor enables label-free high-throughput imaging in strong scattering

Lensless digital holographic microscopy (LDHM) relies on interference between an unscattered reference wave and a weakly scattered object wave - an assumption that rapidly fails in turbid samples under multiple scattering. To overcome this limitation, we present near-infrared LDHM (NIR-LDHM), a in-line holographic platform that operates up to the silicon cutoff (~1100 nm) using a conventional board-level CMOS sensor designed for visible (VIS) imaging. Using tissue-mimicking milk scattering layers and calibrated resolution targets, we quantify reconstruction performance versus wavelength, scattering strength, and sample-sensor distance. NIR-LDHM maintains resolvable features through scattering layers up to ~1.4 mm, whereas visible regime fails to resolve features below ~350 um. Importantly, despite a detector quantum efficiency of only 0.19% at 1100 nm, robust reconstructions are obtained under low-photon-budget conditions. We further identify advantageous mechanism by which increasing the sample-sensor distance from ~3 to 12 mm improves lateral resolution by twofold under strong scattering. Finally, we demonstrate wide-field, label-free amplitude-phase imaging of uncleared mouse tissues, resolving internal structure in brain and liver slices up to ~250 um and ~60 um, respectively. By extending lensless complex-field microscopy into strongly scattering regimes with minimal hardware changes, this work has relevance to computational imaging through complex media and biophotonics.

physics.optics↗

arXiv2026

DeepBessel: deep learning-based full-field vibration profilometry using single-shot time-averaged interference microscopy

Full-field vibration profilometry is essential for dynamic characterizing microelectromechanical systems (MEMS/MOEMS). Time-averaged interferometry (TAI) encodes spatial information about MEMS or MOEMS vibration amplitude in the interferogram's amplitude modulation using Bessel function (besselogram). Classical approaches for interferogram analysis are specialized for cosine function fringe patterns and therefore introduce reconstruction errors for besselogram decoding. This paper presents the DeepBessel: a deep learning-based approach for single-shot TAI interferogram analysis. A convolutional neural network (CNN) was trained using synthetic data, where the input consisted of besselograms, and the output corresponded to the underlying vibration amplitude distribution. Numerical validation and experimental testing demonstrated that DeepBessel significantly reduces reconstruction errors compared to the state-of-the-art approaches, e.g., Hilbert Spiral Transform (HST) method. The proposed network effectively mitigates errors caused by the mismatch between the Bessel and cosine functions. The results indicate that deep learning can improve the accuracy of full-field vibration measurements, offering new possibilities for optical metrology in MEMS or MOEMS applications.

physics.optics↗

arXiv2025

Low-dose Chemically Specific Bioimaging via Deep-UV Lensless Holographic Microscopy on a Standard Camera

Deep-ultraviolet (DUV) microscopy can provide label-free biochemical contrast by exploiting the intrinsic absorption of nucleic acids, proteins and lipids, offering chemically specific morphological information that complements structural optical thickness contrast from phase-sensitive imaging. However, existing DUV microscopes typically rely on specialized optics and DUV-sensitive cameras, which restrict field of view, increase system complexity and cost, and often require high illumination doses that risk photodamage. Here, we report a low-dose deep-UV lensless holographic microscopy platform that uses standard board-level CMOS sensors designed for visible light, eliminating all imaging optics and dedicated DUV detectors. Our system achieves large field-of-view (up to 116 mm2) DUV imaging with low illumination and label-free phase and chemically specific amplitude contrast. A specialized defocus/wavelength diverse pixel super-resolution reconstruction with total-variation regularization and robust autofocusing halves the effective sensor pixel pitch and yields down to 870 nm lateral resolution. We demonstrate chemically specific, label-free bioimaging on challenging specimens, including Saccharomyces cerevisiae, extracellular vesicles and unstained mouse liver tissue. In liver sections, imaging at λ = 330 nm reveals lipid- and retinoid-rich accumulations that co-localize with Oil Red O staining, enabling label-free identification of hepatic stellate (Ito) cells. This combination of low-dose operation, chemically specific contrast and standard CMOS hardware establishes DUV lensless holographic microscopy as a practical and scalable route to high-content submicron-resolution whole-slide preparation-free bioimaging without exogenous labels.

physics.optics↗

arXiv2025

Speckle suppression in digital in-line holographic microscopy through liquid crystal dynamic scattering

We demonstrate speckle noise reduction in an in-line holographic imaging system using a Zwitterion-doped liquid crystal dynamic scatterer (LCDS) cell diffuser. Integrated into a minimally modified bright-field microscope, the LCDS actively modulates system's spatial coherence. The proposed solution suppresses coherent artifacts without introducing bulky moving parts, while enhancing image resolution and preserving overall system simplicity. Quantitative performance tested on a phase and amplitude test targets, as well as phase-amplitude biological sample, shows significant noise reduction and methods versatility. Though validated in a holographic in-line setup, the approach is applicable to other imaging techniques requiring compact, vibration-free speckle suppression.

physics.optics↗

arXiv2025

Gigavoxel-Scale Multiple-Scattering-Aware Lensless Holotomography

Holotomography (HT) has revolutionized quantitative label-free 3D imaging, yet conventional lens-based implementations are fundamentally constrained in field-of-view (FOV) and imaging depth, limiting their utility for critical high-throughput applications in material and life sciences. Lensless HT (LHT) offers a promising alternative for large-volume examination, however existing approaches fail to accurately reconstruct highly scattering samples over extended depths, which remains a critical challenge in optical imaging field. Here, we introduce a gigavoxel-scale, multiple-scattering-aware LHT with a large FOV (surpassing 0.6 cm2), millimeter-scale axial range and pixel level (~2.4 micron) resolution. Our approach leverages a multi-wavelength, oblique-illumination hologram reconstruction and a robust, automatic illumination angle calibration, which are necessary for precise large-volume 3D holographic reconstruction. Moreover, we propose optimization-driven multi-slice tomographic framework to accurately capture multiple-scattering effects outperforming first order Born/Rytov-based inversions. To rigorously validate our method, we reconstruct bespoke multi-layer two-photon polymerized test structure over a 1.7 mm imaging depth and 25 mm2 FOV, yielding an unprecedented 3D space-bandwidth product exceeding a gigavoxel level. Furthermore, we demonstrate for the first time on-chip label-free imaging of entire 500-micron-thick tissue slice of optically-cleared mouse brain. With the proposed method, we aim to unlock powerful new capabilities for large-scale, quantitative, label-free 3D imaging across biomedicine, neuroscience, material sciences and beyond.

physics.optics↗

arXiv2024

Hybrid iterating-averaging low photon budget Gabor holographic microscopy

One of the primary challenges in live cell culture observation is achieving high-contrast imaging with minimal impact on sample behavior. Quantitative phase imaging (QPI) techniques address this by providing label-free, high-contrast images of transparent samples. Measurement system influence may be further reduced by imaging samples under low illumination intensity (low photon budget - LPB), thereby minimizing photostimulation, phototoxicity, and photodamage, and enabling high-speed imaging. LPB imaging is challenging in QPI due to significant camera shot noise and quantification noise. Digital in-line holographic microscopy (DIHM), working with or without lenses, is a QPI technique known for its robustness to quantification noise. However, reducing simultaneously the camera shot noise and the inherent in-line holographic twin image disturbances remain a critical, yet unaddressed, challenge. This study introduces an innovative iterative Gabor averaging (IGA) algorithm designed specifically for filling this important scientific gap in multi-frame DIHM under LPB conditions. We evaluated the performance of the IGA on simulated data showing that it outperformed traditional algorithms in terms of reconstruction accuracy under high noise conditions. Those results were corroborated by experimental validation involving high-speed imaging of dynamic sperm cells and a phase test target under significantly reduced illumination power. Additionally, the IGA algorithm proved successful in reconstructing optically thin samples, which typically produce low signal-to-noise ratio holograms even under high photon budget conditions. These advancements facilitate photostimulation-free and high-speed imaging of dynamic biological samples and enhance the capability to image samples with extremely low optical thickness, potentially transforming various applications in biomedical and environmental imaging.

physics.optics↗

arXiv2024

Quantitative phase imaging verification in large field-of-view lensless holographic microscopy via two-photon 3D printing

Large field-of-view (FOV) microscopic imaging with high lateral resolution (1-2 microns for high space-bandwidth product) plays a pivotal role in biomedicine and biophotonics, especially within the label-free regime, e.g., for whole slide tissue quantitative analysis and live cell culture imaging. In this context, lensless digital holographic microscopy (LDHM) holds substantial promise. However, one intriguing challenge has been the fidelity of computational quantitative phase imaging (QPI) with LDHM in large FOV. While photonic phantoms, 3D printed by two-photon polymerization (TPP), have facilitated calibration and verification in small FOV lens-based QPI systems, an equivalent evaluation for lensless techniques remains elusive, compounded by issues such as twin-image and beam distortions, particularly towards the detector edges. To tackle this problem, we propose an application of TPP over large area to examine phase consistency in LDHM. In our research, we crafted widefield calibration phase test targets, fabricated them with galvo and piezo scanning, and scrutinized them under single-shot twin-image corrupted conditions and multi-frame iterative twin-image minimization scenarios. By displacing the structures toward the edges of the sensing area, we verified LDHM phase imaging errors across the entire field-of-view, showing less than 12 percent of phase value difference between investigated areas. Interestingly, our research revealed that the TPP technique, following LDHM and Linnik interferometry cross-verification, requires novel design considerations for successful large-area precise photonic manufacturing. Our work thus unveils important avenues toward the quantitative benchmarking of large FOV lensless phase imaging, advancing our mechanistic understanding of LDHM techniques and contributing to their further development and optimization of both phase imaging and fabrication.

physics.optics↗