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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.

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.

Fringe pattern based measurement techniques are the state-of-the-art in full-field optical metrology. They are crucial both in macroscale, e.g., fringe projection profilometry, and microscale, e.g., label-free quantitative phase microscopy. Accurate estimation of the local fringe orientation map can significantly facilitate the measurement process on various ways, e.g., fringe filtering (denoising), fringe pattern boundary padding, fringe skeletoning (contouring/following/tracking), local fringe spatial frequency (fringe period) estimation and fringe pattern phase demodulation. Considering all of that the accurate, robust and preferably automatic estimation of local fringe orientation map is of high importance. In this paper we propose novel numerical solution for local fringe orientation map estimation based on convolutional neural network and deep learning called DeepOrientation. Numerical simulations and experimental results corroborate the effectiveness of the proposed DeepOrientation comparing it with the representative of the classical approach to orientation estimation called combined plane fitting/gradient method. The example proving the effectiveness of DeepOrientation in fringe pattern analysis, which we present in this paper is the application of DeepOrientation for guiding the phase demodulation process in Hilbert spiral transform. In particular, living HeLa cells quantitative phase imaging outcomes verify the method as an important asset in label-free microscopy.

Digital in-line holographic microscopy (DIHM) enables efficient and cost-effective computational quantitative phase imaging with a large field of view, making it valuable for studying cell motility, migration, and bio-microfluidics. However, the quality of DIHM reconstructions is compromised by twin-image noise, posing a significant challenge. Conventional methods for mitigating this noise involve complex hardware setups or time-consuming algorithms with often limited effectiveness. In this work, we propose UTIRnet, a deep learning solution for fast, robust, and universally applicable twin-image suppression, trained exclusively on numerically generated datasets. The availability of open-source UTIRnet codes facilitates its implementation in various DIHM systems without the need for extensive experimental training data. Notably, our network ensures the consistency of reconstruction results with input holograms, imparting a physics-based foundation and enhancing reliability compared to conventional deep learning approaches. Experimental verification was conducted among others on live neural glial cell culture migration sensing, which is crucial for neurodegenerative disease research.

Lensless digital holographic microscopy (LDHM) offers very large field-of-view label-free imaging crucial, e.g., in high-throughput particle tracking and biomedical examination of cells and tissues. Compact layouts promote point-of-case and out-of-laboratory applications. The LDHM, based on the Gabor in-line holographic principle, is inherently spoiled by the twin-image effect, which complicates the quantitative analysis of reconstructed phase and amplitude maps. Popular family of solutions consists of numerical methods, which tend to minimize twin-image upon iterative process based on data redundancy. Additional hologram recordings are needed, and final results heavily depend on the algorithmic parameters, however. In this contribution we present a novel single-shot experimental-numerical twin-image removal technique for LDHM. It leverages two-source off-axis hologram recording deploying simple fiber splitter. Additionally, we introduce a novel phase retrieval numerical algorithm specifically tailored to the acquired holograms, that provides twin-image-free reconstruction without compromising the resolution. We quantitatively and qualitatively verify proposed method employing phase test target and cheek cells biosample. The results demonstrate that the proposed technique enables low-cost, out-of-laboratory LDHM imaging with enhanced precision, achieved through the elimination of twin-image errors. This advancement opens new avenues for more accurate technical and biomedical imaging applications using LDHM, particularly in scenarios where cost-effective and portable imaging solutions are desired.

Exposure to laser light alters cell culture examination via optical microscopic imaging techniques, also based on label-free coherent digital holography. To mitigate this detrimental feature, researchers tend to use a broader spectrum and lower intensity of illumination, which can decrease the quality of holographic imaging due to lower resolution and higher noise. We study the lensless digital holographic microscopy (LDHM) ability to operate in the low photon budget (LPB) regime to enable imaging of unimpaired live cells with minimized sample interaction. Low-cost off-the-shelf components are used, promoting the usability of such a straightforward approach. We show that recording data in the LPB regime (down to 7 uW of illumination power) does not limit the contrast nor resolution of the hologram phase and amplitude reconstruction compared to the regular illumination. The LPB generates hardware camera shot noise, however, to be effectively minimized via numerical denoising. The ability to obtain high-quality, high-resolution optical complex field reconstruction was confirmed using the USAF 1951 amplitude sample, phase resolution test target, and finally, live glial restricted progenitor cells (as a challenging strongly absorbing and scattering biomedical sample). The proposed approach based on severely limiting the photon budget in lensless holographic microscopy method can open new avenues in high-throughout (optimal resolution, large field-of-view and high signal-to-noise-ratio single-hologram reconstruction) cell culture imaging with minimized sample interaction.

Quantitative phase microscopy (QPM) is often based on recording an object-reference interference pattern and its further phase demodulation. We propose Pseudo Hilbert Phase Microscopy (PHPM) where we combine pseudo thermal light source illumination and Hilbert spiral transform phase demodulation to achieve hybrid hardware-software-driven noise robustness and increase in resolution of single-shot coherent QPM. Those advantageous features stem from physically altering the laser spatial coherence and numerically restoring spectrally overlapped object spatial frequencies. Capabilities of the PHPM are demonstrated analyzing calibrated phase targets and live HeLa cells in comparison with laser illumination and phase demodulation via temporal phase shifting and Fourier transform techniques. Performed studies verified unique ability of the PHPM to couple single-shot imaging, noise minimization, and preservation of phase details.

Retrieving object phase from the optical fringe pattern is a critical task in quantitative phase imaging and often requires appropriate image preprocessing (background and noise minimization), especially when retrieving phase from the single-shot fringe pattern image. In this article, for the first time, we propose to adapt the 2D Fast Iterative Filtering (FIF) method for fringe pattern decomposition and develop a novel version of FIF called the 2D fringe pattern Fast Iterative Filtering (fpFIF2), that is tailored for fringe pattern preprocessing. We show the positive influence of fpFIF2 onto fringe pattern filtering comparing to the previous 2D FIF implementation regarding processing speed, quality, and usage comfortability. We also compare the fpFIF2 with other state-of-the-art fringe pattern filtering methods in terms of aiding the Hilbert spiral transform method in phase retrieval. Employing numerical simulations and experimental fringe analysis, we prove that fpFIF2 outperforms reference methods, especially in terms of low-fringe-contrast phase reconstruction quality and decomposition time.