pyDHM: A Python library for applications in digital holographic microscopy

Guerrero-Castañeda, Raúl Fernando; Trujillo, Carlos; Doblas, Ana

doi:10.1371/journal.pone.0275818

Cited by 18 publications

(9 citation statements)

References 62 publications

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“…The diffraction patterns of diatoms close to the spherical source were generated by estimating the Rayleigh Sommerfeld Diffraction formula using the Bluestein Fast Fourier Transform (FFT) method [28]. For planes closer to the sensor, the light propagation was implemented by the Angular spectrum method [29,30] with a modification to account for the change of the lateral magnification factor due to the spherical light illumination. Defocused diatom images were simulated at 5 evenly spaced axial depths, ranging from z = 0.5 to 4.1 mm.…”

Section: Simulated Lensless Datasetmentioning

confidence: 99%

Diatom classification via deep learning using raw holograms captured by a lenless holographic system

Bogue-Jimenez,

Castaneda,

Trujillo

et al. 2024

AI and Optical Data Sciences V

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Digital Lensless Holographic Microscopy (DLHM) is a phase imaging modality that omits the use of lenses or other bulky hardware to recover information from microscopic objects. Deep learning models have been recently used to substitute traditional DLHM reconstruction algorithms and classify samples from the reconstructed amplitude and phase images. In this work, we have investigated using these models to classify diatom samples, circumventing the whole reconstruction process altogether. We have validated our approach using a simulated DLHM dataset by comparing the performance of three typical image-processing learning-based models: AlexNet, VGG16, and ResNet-18.

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Section: Simulated Lensless Datasetmentioning

confidence: 99%

Diatom classification via deep learning using raw holograms captured by a lenless holographic system

Bogue-Jimenez,

Castaneda,

Trujillo

et al. 2024

AI and Optical Data Sciences V

Self Cite

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“…For further details on the hardware aspects of DHM, consult [24]. For a comprehensive understanding of the numerical reconstruction procedures, refer to [36]. Once the complete complex wavefield of the studied sample is recovered, its amplitude 𝐴(𝑥,𝑦) = |𝑜(𝑥, 𝑦)| and phase 𝜑(𝑥,𝑦) = tan -1 ( 𝐼𝑚{𝑜(𝑥, 𝑦)} 𝑅𝑒{𝑜(𝑥, 𝑦)} ) measurements can be accessed.…”

Section: Off-axis Digital Holographic Microscopymentioning

confidence: 99%

Comparative Analysis of Digital Holographic Microscopy and Lensless Holographic Microscopy for Quantitative Phase Imaging

Trujillo,

Obando-Vásquez,

Acosta

et al. 2023

Preprint

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This study provides a detailed comparison of two widely used Quantitative Phase Imaging (QPI) techniques: single-shot off-axis Digital Holographic Microscopy (DHM) and Lensless Holographic Microscopy (LHM). The primary aim is to evaluate and contrast critical aspects of their imaging performance, including spatial phase sensitivity, phase measurement accuracy, and spatial lateral resolution. Employing typical configurations for both DHM and LHM, the study utilizes a customized phase test target featuring linear phase changes introduced by a specially designed linear density attenuation filter. Ground truth data from an Atomic Force Microscope is incorporated to validate the experimental findings. The comparative analysis reveals that DHM and LHM exhibit nearly identical spatial phase sensitivity, with DHM demonstrating a minimal 3.2% measurement error compared to LHM's 4% in height measurement accuracy. Notably, DHM achieves a finer spatial lateral resolution down to 3.1 µm, surpassing LHM's 5.52 µm. While DHM outperforms LHM in precision and resolution, the latter offers advantages in terms of portability and cost-effectiveness. These findings provide valuable insights for researchers and practitioners, aiding in the informed selection of QPI methods based on specific application requirements.

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“…DHM is based on the principle of optical interferometry, which involves the digital recording of the interference pattern formed by the interaction of scattered light from the sample, known as the object beam, with a well-defined reference beam, using a digital image sensor. 9 The reconstruction of the recorded hologram is significantly tied to computational reconstruction processing, which plays a crucial role in providing accurate and reliable information about the sample under examination. 10,11 The optical design of the DHM system has a unique bearing on the choice of computational reconstruction strategies.…”

Section: Introductionmentioning

confidence: 99%

Accurate phase reconstruction in digital holography microscopy using Fresnel biprism

Jaduvanshi,

Pathak,

Narayanamurthy

2024

Quantitative Phase Imaging X

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Digital holography microscopy (DHM) has emerged as a powerful digital holographic imaging technique for dynamic three-dimensional phase reconstruction of microscopic samples. Recently, a Fresnel biprism has been introduced into the conventional DHM arrangement, making the configuration common path, off-axis and eliminating the requirement for different optical components, such as, beam-splitter, mirrors, etc. The recorded hologram in a DHM is mostly contaminated with speckle noise, which makes it difficult to interpret the phase information correctly. In the present work, we investigate the phase reconstruction accuracy in a Fresnel biprism based DHM, utilizing different Fourier terms, in the presence of low and high coherent sources. Different Fourier terms has been realised by modifying the recorded hologram in order to minimize the contribution of speckle noise in the reconstructed phase. Proof-of-concept simulation and experimental results are included for blood sample to demonstrate the accuracy in phase reconstruction in a Fresnel biprism based DHM.

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pyDHM: A Python library for applications in digital holographic microscopy

Cited by 18 publications

References 62 publications

Diatom classification via deep learning using raw holograms captured by a lenless holographic system

Diatom classification via deep learning using raw holograms captured by a lenless holographic system

Comparative Analysis of Digital Holographic Microscopy and Lensless Holographic Microscopy for Quantitative Phase Imaging

Accurate phase reconstruction in digital holography microscopy using Fresnel biprism

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