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Digital holographic microscopy is a powerful label-free imaging technique that enables quantitative measurement of optical phase, morphology, and dynamics of biological specimens. Despite its strong potential, its adoption in biological laboratories remains limited by the lack of accessible, integrated, and user-friendly software that supports the full workflow, from hologram acquisition and numerical reconstruction to quantitative analysis and visualization.

HoloBio is a comprehensive and modular software platform that bridges this gap. HoloBio integrates hologram reconstruction, phase compensation, numerical propagation, speckle analysis, quantitative phase imaging, particle tracking, and microstructure characterization within a unified graphical interface. The platform supports both offline and real-time operation and allows users to interactively explore amplitude, phase, and derived physical maps without requiring advanced programming or optical expertise. Using experimental biological samples, we demonstrate that HoloBio enables reliable, reproducible, and quantitative biological analysis. By providing an open, extensible, and user-oriented environment, HoloBio lowers the barrier to adopting digital holographic microscopy in biological and biomedical research, facilitating reproducible workflows and accelerating data-driven discovery

HoloBio is an open-source Python library and GUI for quantitative analysis in Digital Holographic Microscopy (DHM). It supports real-time and offline modes, various optical setups (lens-based and lensless), and provides advanced tools for analyzing biological samples.

The OpenSRQPI App is an open-access, GUI-based computational platform for super-resolution reconstruction in QPI using both structured and oblique illumination. Whereas open-source tools for structured illumination microscopy (SIM), including fairSIM, SIMToolbox, and Open-3DSIM, have enabled widespread adoption of fluorescent-based SIM in life sciences by lowering computational barriers and offering robust image validation workflows, such resources are notably absent in the domain of QPI. Unlike qualitative fluorescence SIM imaging, QPI requires precise phase estimation across the entire field of view, where even small inaccuracies can distort quantitative analysis.

This repository hosts the complete distribution of a MATLAB application developed in version R2025a, designed for advanced processing and compensation of digital holograms acquired under oblique illumination and structured illumination conditions. To ensure broad accessibility, we provide not only the open-source code but also a compiled standalone executable and a packaged installer, allowing users to run the software without requiring a full MATLAB environment. In addition, the repository includes a set of test datasets to facilitate immediate experimentation and validation of the app’s functionality. For oblique illumination, we supply simulated holograms both in ideal, noise-free conditions and in noisy scenarios with signal-to-noise ratios (SNR) ranging from 5 to 40, enabling users to evaluate performance across different noise levels. For structured illumination, the test material comprises noise-free simulations alongside real experimental images, offering a comprehensive reference for both synthetic and practical cases. By combining code, executables, and curated datasets, this repository is intended to serve as a complete platform for researchers and practitioners to explore, test, and extend the capabilities of the developed application.

Digital Holographic Microscopy (DHM) is a powerful technique for label-free imaging, particularly useful in biomedical applications. However, the accuracy of DHM measurements often depends on compensating for phase aberrations caused by optical setups and sample properties. However, when imaging intricate biological samples, such as those involving high spatial frecuency objects or rapidly changing contrast details, conventional phase compensation method usually fail. Therefore, this method is specially suited to properly compensate and image these kinds of samples.

This repository implements the Vortex-Legendre Method, a two-step computational approach that:

1. Utilizes numerical optical vortices for precise sub-pixel localization of diffraction orders, enabling robust tilt aberration correction.

2. Applies Legendre Polynomial Fitting (LPF) to address residual higher-order aberrations, ensuring fully compensated phase maps.

In this undergraduate research project, we aim to investigate the performance of the PCA-based method for phase reconstruction in an in-line DHM system. Specifically, we will evaluate the accuracy and robustness of PCA-based phase reconstruction algorithms based on the number of phase-shifted images and the value of the phase step. The PCA algorithm will be tested under both noiseless and noisy conditions to assess its reliability in practical imaging scenarios. This research project seeks to contribute to the understanding of the potential of PCA as a viable method for phase reconstruction in DHM and its applicability in real-world imaging applications.

This MATLAB code enables the indirect measurement of the chlorophyll molecules present when a green laser diode illuminates the oil sample. Oil blends can be classified into five classes (no olive oil, light olive oil, medium olive oil, olive oil, and extra virgin olive oil) by quantifying the ratio of the red channel versus the green channel along the laser illumination path from a color image.

Digital Holographic Microscopy (DHM) enables the three-dimensional (3D) reconstruction of quantitative phase distributions from a defocused hologram. We have developed a synergistic computational framework to simultaneously compensate for the linear tilt introduced in off-axis DHM systems and focus the defocused holograms by minimizing a cost function, providing in-focus reconstructed phase images without phase distortions. 

The proposed Semi-heuristic phase compensation (SHPC) method is an alternative approach for reconstructing phase maps with reduced processing time compared with the brute-force algorithms while avoiding the local-minimum problem of the heuristic proposals. This algorithm is based on nested searches in which the grid size in every iteration is systematically reduced to optimize the compensation time. This algorithm also have a dynamic version called D-SHPC for compensating holographic videos of dynamic samples. Both algorithms are accurate in phase reconstructions and fast enough to compensate full FOV (1280x960 pixels) holograms at rates of 5 FPS.

 We have proposed a global stress detection framework combining eight HRV features and an RF model with a classifying power of 99% or higher during individual testing using two benchmark datasets (i.e., WESAD and SWELL datasets). The proposed global stress detection framework is available on GitHub. We used Google Collab with Python 3 to investigate the proposed global stress framework. The code was implemented using Python libraries such as TensorFlow, Matplotlib, NumPy, Pandas, and Karas. The computational specs for the algorithm are 12 GB RAM using Google Collaboratory.

Among the optical configuration of the imaging system in DHM, imaging systems operating in a non-telecentric regime are the most common ones. The spherical wavefront introduced by the non-telecentric DHM system must be compensated to provide undistorted phase measurements. Here, we present a clear and simple procedure for a total compensation algorithm for non-telecentric DHM holograms independent of the sample's size (i.e., no requiring sample-free field of view within the hologram). The proposed method has been implemented in MATLAB 2021a and Python 3.7.1. Users of the proposed method should have installed the Optimization and Global Optimization MATLAB toolboxes and the scipy library from Python.

Confocal microscopes are known for optically removing the out-of-focus information (i.e., blur) in each transverse section of the sample’s volume, providing a more accurate three-dimensional image of thick microscopic samples than widefield microscopes. In this work, we have designed and evaluated a confocal microscope using off-of-shelf optical components from Thorlabs’ catalog, one of the major optical manufacturers. The design and evaluation have been implemented using Zemax OpticStudio, the standard optical system design software. Here, we have also reported a practical protocol for building a confocal microscope using sequential mode.

More information and Github repository at: https://oirl.github.io/Design-Confocal.github.io/

Spectrometers are optical tools to measure the spectral composition of a beam of light. Key features of an optimal spectrometer are: cost, variation of the spot size along the spectral range, spot size compared to the diffraction limit, and minimum presence of aberrations. Here, we present different optical designs to focus the light emerging from a diffraction grating.

More information and Github repository at: https://oirl.github.io/Design-espectometer.github.io/

The hallmark of polarization-sensitive microscopy is the estimation of polarimetric information of samples, making them a suitable tool for detecting and diagnosis different type of cells and tissues in life sciences. In this work, we describe a practical procedure for measuring the Mueller matrix of samples using a bright-field microscope using 36 intensity-based images. The hallmark of the proposed method is the retrieval of the Mueller matrix across the transverse section of a sample, being suitable for analyzing biological samples. The method has been implemented in Python and MATLAB.