Accurate and label-free quantitative phase imaging (QPI) plays a crucial role in advancing diagnostic techniques that streamline histology and diagnostic procedures by minimizing sample preparation time, resources, and requirements. Although Digital Holographic Microscopy (DHM) has become a prominent tool within QPI, its diffraction-limited resolution has hindered broader adoption of QPI-DHM. The use of structured and oblique illumination in DHM platforms has overcome the resolution limit, advancing QPI-DHM technology to super-resolution QPI. Despite demonstrated success, adoption of super-resolution DHM (SR-DHM) in clinical and biomedical research remains limited by the absence of a standardized reconstruction algorithm capable of delivering quantitatively accurate, distortion-free super-resolved phase images. This work presents OpenSRQPI, the first standardized computational framework for super-resolution phase reconstruction in DHM systems, whether using structured or oblique illumination. Through its intuitive graphical user interface (GUI) and minimal parameter requirements, OpenSRQPI reduces the technical barrier for non-experts, making super-resolution QPI broadly accessible, enabling new studies of live-cell dynamics, subcellular structure, and tissue morphology.

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Digital holographic microscopy (DHM) offers label-free, high-resolution quantitative phase imaging, making it a powerful tool for real-time visualization of dynamic biological processes. However, when imaging intricate biological samples with detailed cellular structures—such as tissues containing diverse cell types, fine organelles, or intricate vascular networks— and with subtle variations in refractive index, the accuracy of phase reconstruction is compromised by several types of phase aberrations. These aberrations include tilt distortions due to the off-axis configuration, quadratic phase errors introduced by microscope objectives, and additional higher-order aberrations caused by sample heterogeneity and imperfections in the optical system. Traditional compensation techniques either rely on iterative computations, multi-shot acquisitions, or additional optical components, thereby limiting their applicability in fast, real-time imaging scenarios. This work introduces a novel, fully computational hybrid approach—termed the vortex-Legendre method—that addresses these limitations. This method leverages a numerical optical vortex to achieve precise, sub-pixel localization of the +1 diffraction order for tilt aberration correction, followed by Legendre polynomial fitting to efficiently compensate for residual higher-order aberrations. Validation experiments on calibrated phase targets and various biological samples demonstrate that the vortex-Legendre method preserves high-frequency details and delivers consistent performance across both telecentric and non-telecentric imaging configurations. Compared to state-of-the-art approaches, this method improves phase compensation accuracy while maintaining computational efficiency, paving the way for high-fidelity quantitative phase imaging of complex biomedical samples.

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Accurate and reproducible measurement of optical parameters such as refractive index (RI) and thickness is central to quantitative phase imaging (QPI) and its applications in biology and material science. A long-standing challenge in QPI systems is the intrinsic coupling between RI and thickness in the reconstructed phase distribution, which limits the metrological value of QPI for uniquely and robustly separating the sample’s RI and physical thickness. Unlike previous decoupling strategies that rely on multi-wavelength or tomographic recording, this work presents a measurement-oriented computational framework, the Cauchy-based Inverse Phase Hybrid Error Reduction (CIPHER) algorithm, designed to decouple RI and thickness from a single reconstructed phase map. By incorporating Cauchy’s dispersion model into the inverse problem, CIPHER provides a physically constrained measurement model that reduces solution ambiguity and improves estimation stability. The algorithm incorporates GPU acceleration and an optimized CIPHER implementation based on a coarse-to-fine full-grid search with fast thickness selection and deterministic tie-breaking, preserving the full-grid solution while reducing computation time by nearly an order of magnitude for high-resolution phase imaging applications. The performance of CIPHER is validated using both simulated and experimental datasets, including a calibrated phase target with traceable thickness standards and an array of microlenses with unknown parameters. Experimental results demonstrate that CIPHER accurately estimates thickness and RI maps, achieving accuracy above 95% for both calibrated and non-calibrated targets. By enabling accurate RI and thickness measurements without assumptions about sample geometry or the surrounding medium, CIPHER advances the state of the art in optical metrology. CIPHER represents a generalizable measurement model for phase-based systems, with potential applications spanning biomedical diagnostics, materials inspection, and optical manufacturing where traceable, uncertainty-aware quantification of optical parameters is required. 

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Illuminating a microscopic sample with a structured illumination (SI) pattern in digital holographic microscopy (DHM) enables the encoding of high sample’s spatial frequencies beyond the compact support of the native microscopic imaging system into the +1 diffraction term, providing super-resolved phase images after applying a reconstruction computational method. The quality of the reconstructed phase map relies heavily on the correct computational reconstruction method. One of the most important steps is the correct demodulation of the super-resolved components encoded within the +1 diffraction term. This work presents a generalized reconstruction framework for SI-DHM that automatically demodulates the two laterally shifted object spectrums without prior knowledge of the phase shifts between the recorded holograms and automatically compensates for the linear phase term of each shifted object spectrum. The proposed framework reconstructs super-resolved phase images with minimum inputs from the user, only requiring the two recorded phase-shifted holograms, the pixel size, and the source’s wavelength. The framework's performance has been validated using simulated and experimental holograms for two different SI-DHM systems.

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HoloStream is a GPU-powered high-speed user interface designed for holographic microscopy imaging. The platform reconstructs quantitative phase images rapidly for off-axis digital holographic microscopy (DHM) systems operating in the telecentric regime. By optimizing critical computational procedures, the HoloStream app reconstructs phase maps of 1280 × 960 pixels up to 11 frames per second (FPS) in live visualization and 90 fps for pre-recorded holographic videos. This real-time phase compensation is performed by upgrading the semi-heuristic phase compensation (SHPC) algorithm through GPU acceleration via PyCUDA. The interface also offers a user-friendly experience with flexible control over acquisition modes, visualization formats, and 3D tracking functionalities. Experimental validation demonstrates the platform's capability to achieve fast and accurate phase imaging for live microscopy applications. The HoloStream app advances DHM technology by combining high-speed processing with ease of use, making it suitable for a wide range of DHM imaging applications.

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Supervised deep learning models have enabled super-resolution imaging in several microscopic imaging modalities, increasing the spatial lateral bandwidth of the original input images beyond the diffraction limit. Despite their success, their practical application poses several challenges in terms of the amount of training data and its quality, requiring the experimental acquisition of large, paired databases to generate an accurate generalized model whose performance remains invariant to unseen data. Cycle-consistent generative adversarial networks (cycleGANs) are unsupervised models for image-to-image translation tasks that are trained on unpaired datasets. This project introduces a cycleGAN framework specifically designed to increase the lateral resolution limit in confocal microscopy by training a cycleGAN model using low- and high-resolution unpaired confocal images of human glioblastoma cells. Training and testing performances of the cycleGAN model have been assessed by measuring specific metrics such as background standard deviation, peak-to-noise ratio, and a customized frequency content measure. Our cycleGAN model has been evaluated in terms of image fidelity and resolution improvement using a paired dataset, showing superior performance than other reported methods. This work highlights the efficacy and promise of cycleGAN models in tackling super-resolution microscopic imaging without paired training, paving the path for turning home-built low-resolution microscopic systems into low-cost super-resolution instruments by means of unsupervised deep learning.

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Digital holographic microscopy (DHM) enables the three-dimensional (3D) reconstruction of quantitative phase distributions from a defocused hologram. Traditional computational algorithms follow a sequential approach in which one first reconstructs the complex amplitude distribution and later applies focusing algorithms to provide an in-focus phase map. In this work, we have developed a synergistic computational framework to compensate for the linear tilt introduced in off-axis DHM systems and autofocus the defocused holograms by minimizing a cost function, providing in-focus reconstructed phase images without phase distortions. The proposed computational tool has been validated in defocused holograms of human red blood cells and three-dimensional images of dynamic sperm cells.

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Citation:

R. Castaneda, C. Trujillo, and A. Doblas, "In-focus quantitative phase imaging from defocused off-axis holograms: synergistic reconstruction framework," Opt. Letters 48(23), 6244-6247 (2023). doi: 10.1364/OL.506400

Digital holographic microscopy (DHM) is a cutting-edge interferometric technique to recover the complex wavefield scattered by microscopic samples from digitally recorded intensity patterns. In off-axis DHM, the challenge is digitally generating the reference wavefront replica to compensate for the tilt between the interfering waves. Current methods to estimate the reference wavefront's parameters rely on brute-force grid searches or heuristics algorithms. Whereas brute-forced searches are time-consuming and impractical for video-rate quantitative phase imaging and analysis, applying heuristics methods in holographic videos is limited since the phase background level occasionally changes between frames. A semi-heuristic phase compensation (SHPC) algorithm is proposed to address these challenges to reconstruct phase images with minimum distortion in the full field-of-view (FOV) from holograms recorded by a telecentric off-axis digital holographic microscope. The method is tested with a USAF test target, smearing red blood cells and alive human sperm. The SHPC method provides accurate phase maps as the reference brute-force method but with a 92-fold reduction in processing time. Furthermore, this method was tested for reconstructing experimental holographic videos of dynamic specimens, obtaining stable phase values and minimal differences in the background between frames. This proposed method provides state-of-the-art phase reconstructions with high accuracy and stability in holographic videos, allowing the successful XYZ tracking of single-moving sperm cells.

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Citation:

S. Obando-Vasquez, A. Doblas, and C. Trujillo, “ Semi-heuristic phase compensation in digital holographic microscopy for stable and accurate quantitative phase imaging of moving objects,” Opt. Lasers Eng. 174, 107937 (2024). doi: 10.1016/j/optlaseng.2023.107937

Quantitative phase imaging (QPI) via Digital Holographic microscopy (DHM) has been widely applied in material and biological applications. The performance of DHM technologies relies heavily on computational reconstruction methods to provide accurate phase measurements. Among the optical configuration of the imaging system in DHM, imaging systems operating in a non-telecentric regime are the most common ones. Nonetheless, the spherical wavefront introduced by the non-telecentric DHM system must be compensated to provide undistorted phase measurements. The proposed reconstruction approach is based on previous work from Kemper’s group. Here, we have reformulated the problem, reducing the number of required parameters needed for reconstructing phase images to the sensor pixel size and source wavelength. The developed computational algorithm can be divided into six main steps. In the first step, the selection of the +1-diffraction order in the hologram spectrum. The interference angle is obtained from the selected +1 order. Secondly, the curvature of the spherical wavefront distorting the sample’s phase map is estimated by analyzing the size of the selected +1 order in the hologram’s spectrum. The third and fourth steps are the spatial filtering of the +1 order and the compensation of the interference angle. The next step involves the estimation of the center of the spherical wavefront. An optional final optimization step has been included to fine-tune the estimated parameters and provide fully compensated phase images. Because the proper implementation of a framework is critical to achieve successful results, we have explicitly described the steps, including functions and toolboxes, required for reconstructing phase images without distortions. As a result, we have provided open-access codes and a user interface tool with minimum user input to reconstruct holograms recorded in a non-telecentric DHM system

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If you want to download the raw codes written in MATLAB and Python, a MATLAB GUI, manual, and the non-telecentric holograms, please click here!

Examples of how to use the MATLAB GUI is found in our YouTube channel!

Citation:

B. Bogue-Jimenez, C. Trujillo, and A. Doblas, “Comprehensive Tool for a Pase Compensation Reconstruction Method in Digital Holographic Microscopy Operating in Non-Telecentric Regime,” Plos ONE 18(9), e0291103 (2023).

pyDHM is an open-source Python library aimed at Digital Holographic Microscopy (DHM) applications. The pyDHM is a user-friendly library written in the robust programming language of Python that provides a set of numerical processing algorithms for reconstructing amplitude and phase images for a broad range of optical DHM configurations. The pyDHM implements phase-shifting approaches for in-line and slightly off-axis systems and enables phase compensation for telecentric and non-telecentric systems. In addition, pyDHM includes three propagation algorithms for numerical focusing complex amplitude distributions in DHM and digital holography (DH) setups. We have validated the library using numerical and experimental holograms.

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Examples of how to install and use it are found in our YouTube channel!

Citation:

R. Castañeda, C. Trujillo, and A. Doblas, "pyDHM: A Python library for applications in digital holographic microscopy," PLoS ONE 17(10): e0275818 (2022) . https://doi.org/10.1371/journal.pone.0275818

The conventional reconstruction method of off-axis digital holographic microscopy (DHM) relies on computational processing that involves spatial filtering of the sample spectrum and tilt compensation between the interfering waves to accurately reconstruct the phase of a biological sample. Additional computational procedures such as numerical focusing may be needed to reconstruct free-of-distortion quantitative phase images based on the optical configuration of the DHM system. Regardless of the implementation, any DHM computational processing leads to long processing times, hampering the use of DHM for video-rate renderings of dynamic biological processes. In this study, we report on a conditional generative adversarial network (cGAN) for robust and fast quantitative phase imaging in DHM. The reconstructed phase images provided by the GAN model present stable background levels, enhancing the visualization of the specimens for different experimental conditions in which the conventional approach often fails. The proposed learning-based method was trained and validated using human red blood cells recorded on an off-axis Mach–Zehnder DHM system. After proper training, the proposed GAN yields a computationally efficient method, reconstructing DHM images seven times faster than conventional computational approaches.

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Citation:

R. Castaneda, C. Trujillo, and A. Doblas, “Video-Rate Quantitative Phase Imaging Using a Digital Holographic Microscope and a Generative Adversarial Network,” Sensors 21(23), 8021 (2021).

This works presents a reconstruction algorithm to recover the complex object information for an off-axis digital holographic microscope (DHM) operating in the telecentric regimen. We introduce an automatic and fast method to minimize a cost function that finds the best numerical conjugated reference beam to compensate the filtered object information, eliminating any undesired phase perturbation due to the tilt between the reference and object waves. The novelties of the proposed approach, to the best of our knowledge, are a precise estimation of the interference angle between the object and reference waves, reconstructed phase images without phase perturbations, and reduced processing time. The method has been validated using a manufactured phase target and biological samples.

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Citation:

• R. Castaneda and A. Doblas, “Fast-iterative automatic reconstruction method for quantitative phase image with reduced phase perturbations in off-axis digital holographic microscopy,” Applied Optics  60 (32), 10214-10220 (2021). https://doi.org/10.1364/AO.437640(2021)

Images recorded by coherent imaging systems, including laser-based photography, digital holography (DH), and digital holographic microscopy (DHM), are severely distorted by speckle noise.  We have worked on a single-shot image processing method to reduce the speckle noise, named as hybrid median-mean filter (HM2F). The HM2F is based on the average of conventional median-filtered images with different kernel size. The synergic combination of the median filter and mean approach provides a denoised image with reduced speckle contrast while the spatial resolution is kept up to 97% from the original value. The HM2F method is compared with the conventional median filter approach (CMF), the 3D Block Matching (BM3D) filter, the non-local means (NLM) filter, the 2D windowed Fourier transform filter (WFT2F), and the Wiener filter using different speckle-distorted images to benchmark its performance. Based on the experimental results and the simplicity of the technique, HM2F is proposed as an effective denoising tool for reducing the speckle noise in laser-based photography, DH, and DHM.

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Citation:

• R. Castaneda, J. Garcia-Sucerquia and A. Doblas, “Speckle noise reduction in coherent imaging systems via hybrid median-mean filter,” Optical Engineering  60(12), 123107 (2021). https://doi.org/10.1117/1.OE.60.12.123107

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Citation:

• R. Castaneda, C. Buitrago-Duque, J. Garcia-Sucerquia, and A. Doblas, “Fast-iterative blind phase-shifting digital holographic microscopy using two images,” Appl. Opt. 59(24) 7469-7476 (2020).

• A. Doblas, C. Buitrago-Duque, A. Robinson, J. Garcia-Sucerquia, and, “Phase-Shifting Digital Holographic Microscopy with iterative blind reconstruction algorithm,” Appl. Opt., 58(34), G311-G317 (2019).

The performance of structured illumination microscopy (SIM) is hampered in many biological applications due to the inability to modulate the light when imaging deep into the sample. This is in part because sample-induced aberration reduces the modulation contrast of the structured pattern. In this paper, we present an image restoration approach suitable for processing raw incoherent-grid-projection SIM data with a low fringe contrast. Restoration results from simulated and experimental ApoTome SIM data show results with an improved signal-to-noise ratio (SNR) and optical sectioning compared to the results obtained from existing methods, such as 2D demodulation and 3D SIM deconvolution. Our proposed method provides satisfactory results (quantified by the achieved SNR and normalized mean square error) even when the modulation contrast of the illumination pattern is as low as 7%. 

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Citation:

N. Patwary, A. Doblas, and C. Preza, “Image restoration approach to address reduced modulation contrast in structured illumination microscopy,” Biomed. Opt. Express 9(4), 1630-1647 (2018).