Digital Holographic Interferometry Research Articles

Simultaneous estimation of multiple order phase derivatives using deep learning method in digital holographic interferometry

For non-contact deformation testing, digital holographic interferometry is a prominent optical technique where the first and second order interference phase derivatives directly embed information about the strain and curvature distributions of a deformed object. Hence, reliable extraction of multiple order phase derivatives is of great practical significance; however, this problem is marred by several challenges such as the need of multiple differentiation operations, complex shearing operations and performance degradation due to noise. In this paper, we introduce a deep learning approach for the direct and simultaneous estimation of first and second order phase derivatives in digital holographic interferometry. Our method's performance is demonstrated via rigorous numerical simulations exhibiting wide range of additive white Gaussian noise and speckle noise. Moreover, we substantiate the practical efficacy of our proposed method for processing deformation fringes acquired via digital holographic interferometry.

Digital Holographic Interferometry for Micro-Deformation Analysis of Morpho Butterfly Wing

In this study, we present an analysis of deflections in a Morpho butterfly wing using digital holographic interferometry (DHI). Our methodology revolves around an off-axis lensless Fourier holographic setup, using laser excitation to induce deflections in the object. The implementation of a DHI setup, tailored for rapid monitoring of micro-deformation, is a central aspect of our research. We offer an overview of the theoretical foundations of this technique, complemented by both experimental and numerical results aimed at validating our findings. We designed an optical setup that enhanced both laser illumination and hologram reconstruction for the sample. The experimental findings decisively show that the proposed method is effective for rapid deformation analysis. The deformation of the wing can be measured with micro-meter accuracy thanks to numerical analysis.

Speckle denoising based on Swin-UNet in digital holographic interferometry

Speckle noise, mechano-physical noise, and environmental noise are inevitably introduced in digital holographic coherent imaging, which seriously affects the quality of phase maps, and the removal of non-Gaussian statistical noise represented by speckle noise has been a challenging problem. In the past few years, deep learning methods based on convolutional neural networks (CNNs) have made good progress in removing Gaussian noise. However, they tend to fail when these deep networks designed for Gaussian noise removal are used to remove speckle noise. Recently, numerous studies have employed CNNs to address the issue of degraded speckle images, yielding encouraging results. Nevertheless, the degradation of speckle noise that is simulated in isolation is limited and insufficient to encompass the increasingly complex DHI noise environment. This paper presents what we believe to be a novel approach to simulating complex noise environments by multiplexing simulated Gaussian noise and speckle noise. The noise resulting from aliasing does not adhere to the statistical laws of the noise prior to aliasing, which poses a more challenging task for the noise-reduction algorithms utilized in neural networks. Consequently, in conjunction with the capacity of the Swin Transformer to model multi-scale features, this paper proposes a DHI speckle denoising approach based on Swin-UNet. In this paper, Gaussian, speckle, and blending noise datasets with different noise densities are constructed for training and testing by numerical simulation, and generalizability tests are performed on 1,100 randomly selected open-source holographic tomography (HT) noise images at Warsaw University of Technology and 25 speckle images selected from DATABASE. All test results are quantitatively evaluated by three evaluation metrics: mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). All convolutional neural network (CNN) algorithms are evaluated qualitatively based on the number of parameters, floating point operations, and denoising time. The results of the comparison demonstrate that the denoising algorithm presented in this paper exhibits greater stability, accuracy, and generalizability.

Fringe pattern normalization using conditional Generative Adversarial Networks

Fringe pattern normalization is an important pre-processing operation in optical fringe analysis, and is usually required as the initial step for several phase retrieval methods. However, noise and rapid amplitude fluctuations in the fringe pattern pose significant challenges for fringe pattern normalization. In this paper, we propose a robust approach for fringe normalization using conditional Generative Adversarial Network, which works on the principle of image-to-image translation with conditioning on input images. The performance of the proposed method under different noise conditions and amplitude variations is demonstrated using numerical simulations. Further, the practical applicability of the proposed method is also tested with experimental data obtained in digital holographic interferometry.

Unsupervised speckle denoising in digital holographic interferometry based on 4-f optical simulation integrated cycle-consistent generative adversarial network

The speckle noise generated during digital holographic interferometry (DHI) is unavoidable and difficult to eliminate, thus reducing its accuracy. We propose a self-supervised deep-learning speckle denoising method using a cycle-consistent generative adversarial network to mitigate the effect of speckle noise. The proposed method integrates a 4-f optical speckle noise simulation module with a parameter generator. In addition, it uses an unpaired dataset for training to overcome the difficulty in obtaining noise-free images and paired data from experiments. The proposed method was tested on both simulated and experimental data, with results showing a 6.9% performance improvement compared with a conventional method and a 2.6% performance improvement compared with unsupervised deep learning in terms of the peak signal-to-noise ratio. Thus, the proposed method exhibits superior denoising performance and potential for DHI, being particularly suitable for processing large datasets.

Impact of optical fiber-based photo-activation on dental composite polymerization

ObjectivesThe study aimed to introduce a novel two-step optical fiber-based photo-activation of dental resin-based composites (RBCs) for reducing polymerization shrinkage stress (PSS). MethodsProposed protocol design – in the first step, two flexible plastic optical fibers connected to a dental light curing unit (LCU), were used as light guides inserted into the filling to initiate low-irradiance polymerization from within; in the second step, fibers were extracted and remaining voids were filled with RBC, followed by conventional high-irradiance curing to finalize polymerization. Three bulk-fill RBCs were tested (Beautifil-Bulk Restorative, Filtek Bulk-fill Posterior, Tetric PowerFill) using tooth cavity models. Three non-invasive examination techniques were employed: Digital Holographic Interferometry, Infrared Thermography, and Raman spectroscopy for monitoring model deformation, RBC temperature change, and degree of conversion (DC), respectively. A control group (for each examined RBC) underwent conventional photo-activation. ResultsThe experimental protocol significantly reduced model deformation by 15 – 35 %, accompanied by an 18 – 54 % reduction in RBC temperature change, emphasizing the impact of thermal shrinkage on PSS. Real-time measurements of deformation and temperature provided indirect insights into reaction dynamics and illuminated potential mechanisms underlying PSS reduction. After a 24-hour dark-storage period, DC outcomes comparable to conventional curing were observed, affirming the clinical applicability of the method. ConclusionsProtocol involving the use of two 1.5 mm fibers in the first step (300 mW/cm2 x 10 s), followed by a second conventional curing step (1000 mW/cm2 x 10 s), is recommended to achieve the desired PSS reduction, while maintaining adequate DC and ensuring efficient clinical application. Clinical SignificanceObtained PSS reduction offers promise in potentially improving the performance of composite restorations. Additionally, leveraging the flexibility of optical fibers improves light guide approach for restorations on posterior teeth. Meanwhile, implementation in clinical practice is easily achievable by coupling the fibers with commercial dental LCUs using the provided plastic adapter.

Guided wave resonance-based digital holographic microscopy for high-sensitivity monitoring of the refractive index.

Surface plasmon resonance holographic microscopy (SPRHM) has been employed to measure the refractive index but whose performance is generally limited by the metallic intrinsic loss. Herein we first, to our knowledge, utilize guided wave resonance (GWR) with low loss to realize the monitoring of the refractive index by integrating with digital holographic microscopy (DHM). By depositing a dielectric layer on a silver film, we observe a typical GWR in the dielectric layer with stronger field enhancement and higher sensitivity to the surrounding refractive index compared to the silver film-supported SPR, which agrees well with calculations. The innovative combination of the GWR and DHM contributes to the highly sensitive dynamic monitoring of the surrounding refractive index variation. Through the measurement with DHM, we found that the GWR presents an excellent sensitivity, which is 2.6 times higher than that of the SPR on the silver film. The results will pave a new pathway for digital holographic interferometry and its applications in environmental and biological detections.

Refocus criterion from image-plane speckle correlation in digital holographic interferometry.

In digital holography and holographic interferometry, refocusing to the correct image plane can be challenging and may be obtained by various metrics. This paper proposes a digital refocus approach utilizing the linear relationship between in-plane speckle motion and defocus as a response to an induced phase gradient. The theory based on cross-correlations between pairs of intensity images reconstructed at different distances from the recording plane is discussed. Two simple metrics, based on the cross-correlation properties of the reconstructed speckle images, are proposed and evaluated utilizing both simulations and experiments. Experiments exhibit similar trends in which the estimate of the correct reconstruction distance differs by a small amount between the two metrics. The difference is found less than 1% in the estimate of the true reconstruction distance. The results show that either metric is able to yield a sufficient reconstruction distance for the reconstruction of the image plane.

Temperature fields in liquids: A comparison of CFD simulations with Digital Holographic Interferometry and thermocouples

Temperature fields in liquids: A comparison of CFD simulations with Digital Holographic Interferometry and thermocouples

Defect Isolation from Whole to Local Field Separation in Complex Interferometry Fringe Patterns through Development of Weighted Least-Squares Algorithm

In this paper, based on Gaussian 1σ-criterion and histogram segmentation, a weighted least-squares algorithm is applied and validated on digital holographic speckle pattern interferometric data to perform phase separation on the complex interference fields. The direct structural diagnosis tool is used to investigate defects and their impact on a complex antique wall painting of Giotto. The interferometry data is acquired with a portable off-axis interferometer set-up with a phase-shifted reference beam coupled with the object beam in front of the digital photosensitive medium. A digital holographic speckle pattern interferometry (DHSPI) system is used to register digital recordings of interferogram sequences over time. The surface is monitored for as long as it deforms prior to returning to its initial reference equilibrium state prior to excitation. The attempt to separate the whole vs. local defect complex amplitudes from the interferometric data is presented. The main aim is to achieve isolation and visualization of each defect’s impact amplitude in order to obtain detailed documentation of each defect and its structural impact on the surface for structural diagnosis purposes.

Dual-wavelength digital holographic interferometry for technical applications

Dual-wavelength digital holographic interferometry for technical applications

Architectural assessment of wall paintings using a multimodal and multi-resolution diagnostic approach: The test site of the Brancacci chapel in Firenze

Based on the analysis of the renaissance wall paintings by Masaccio, Masolino, and Filippino Lippi in the Brancacci chapel in Firenze, this paper discusses the use of complementary non-destructive techniques based on microwave and optical methods for the characterization of the structural integrity of the wall paintings and their support in masonry. The selected non-destructive techniques are the InfraRed Thermography (IRT), Ground Penetrating Radar (GPR), Microwave Reflectometry (MWR), and Digital Holographic Speckle Pattern Interferometry (DHSPI). In particular, the paper analyses the in-situ applicability of these techniques for the identification of the sequence of past interventions during centuries (stratigraphy analysis) and decay phenomena and defects, such as out-of-plumb or swelling area/elements, detachments, cracks and voids inside the wall. The results are compared with data obtained by means of consolidated techniques and methods, such as the Photogrammetry (performed by Structure from Motion method) and knocking test. The last one is normally used by restorers and conservators to recognize the presence of detachments. The proposed diagnostic strategy provides a survey from large scale by means of imaging techniques, to small scale increasing the spatial resolution thanks to the scanning of the surface by means of spot techniques. Therefore, the macroscopic survey of wall paintings was carried out using photogrammetry, in order to also provide metric information, to quantify the sizing out-of-plumb and swelling of the masonry or to locate of cracks, and followed by IRT. This preliminary morphometric survey was, supplemented by GPR, MWR and DHSPI for improve the results of the investigation. By combining these three techniques it was possible to inspect the entire thickness of the masonry (60­70 cm) with resolutions ranging from a few millimetres up to several centimetres. The combination of microwave-based and optical-based methods proved to be a valuable addition to routine methods for the holistic masonry diagnosis. Standard practice based on visual inspection and knocking test can be significantly improved and objectified by the proposed full-field, multi-sensor, multi-resolution approach.

Optical thickness measurement of occluded samples by lens-less Fourier transform digital holography, thermal loading, and machine learning.

Thickness measurements of objects, especially transparent and semi-transparent objects, are essential for their characterization and identification. However, in the case of occluded objects, the optical thickness determination becomes difficult, and an indirect way must be devised. Thermal loading of the objects changes their opto-thermal properties, which will be reflected as a change in their optical thickness. The key to quantifying such occluded objects lies in collecting these opto-thermal signatures. This could be achieved by imaging the changes occurring to a probe wavefront passing through the object while it is being thermally loaded. Digital holographic interferometry is an ideal tool for observing phase changes, as it can be used to compare wavefronts recorded at different instances of time. Lens-less Fourier transform digital holographic imaging provides the phase information from a single Fourier transform of the recorded hologram and can be used to quantify occluded phase objects. Here we describe a technique for the measurement of change in optical thickness of thermally loaded occluded phase samples using lens-less Fourier transform digital holography and machine learning. The advantage of the proposed technique is that it is a single shot, lens-less imaging modality for quasi-real-time quantification of phase samples behind thin occlusions.

Single-shot digital holography of multiple-beam Fizeau holograms for optical phase reconstruction through the focal depth of optical fibers

Multiple-beam Fizeau interference (MBFI) is characterized by its ability to represent the fine features of phase objects. Single-shot digital holographic interferometry (SSDHI) is used for numerical focusing of the recorded holograms. In this work, the two methods are gathered and employed for solving the problem of studying the thick phase objects. The problem of thick phase objects, such as optical fibers, in MBFI is the full focusing of the fiber's optical phase in one-time single shot phase map. The proposed method is designed to collect the optical phase map due to the optical fiber through the focal depth. The main idea is based on the analysis of the MBFI to several two-beam interference sets. Each two-beam interference is treated by SSDHI method to get the optical phase at different planes of focus inside the object. Then, the resultant optical phase due to the optical fiber is collected from these planes of focus. Retrieving the optical phase maps of the multiple-beam holograms through the focal depth, for the first time, is possible using the angular spectrum method. In this case, a laser source with quite long coherence length is used as a light source.The proposed method is applied on two thick samples of, single mode and graded index, optical fibers to reconstruct the resultant optical phase maps through the focal depth. The reconstructed optical phase maps are determined for the harmonics of MBFI holograms’ angular spectra. The method proves its ability to provide more accurate, numerical focusing and phase maps reconstruction. Where, MBFI analyses indicate the magnification of optical phase differences.

The Mechanism of Droplet Thermocapillary Migration Coupled with Multi-Physical Fields

In this paper, the coupling effect of multiphysical fields of droplet migration is deeply studied by constructing a physical model of droplet migration with multiphysical fields. Digital holographic interferometry and particle image velocimetry are used to simultaneously measure the temperature and velocity fields of the mother liquor in the process of droplet migration for the first time. Due to the advancements of measuring, the zero-velocity region is also in the region where the thermal wake appears, four vortexes appear in the droplet migration and the off-axis behavior of double-droplet migration is found. The aim of this work is to analyze the coupling relationship of multiphysical fields, so as to reveal the physical laws of thermocapillary migration of single droplet and multiple droplets with the same phase and heterophase and to study the driving mechanism of the thermocapillary force and the flow of the mother liquor.

A novel measurement method for measuring the concentration-dependent mutual diffusion coefficients based on finite volume method

This paper presents a novel measurement method based on the finite volume method (FVM), which can be used for measuring the concentration-dependent mutual diffusion coefficient D(C) in binary solution. The measurement principle of D was derived by integrating the Fick’s second law in the control volume and establishing discretization equation. By simulating the diffusion process of binary solution, discussed the feasibility of this method. This new method can be used by an experimental system that can monitor changes in concentration distribution, such as optical interferometry. A set of 20 binary mixtures was tested to verify the method. The experimental results showed good agreement with and the literature, which verifies the accuracy of the method. The relative expanded combined uncertainty for the proposed method is estimated to 2.2 %. In addition, D(C) of LiTSFI aqueous solution was measured by the new method based on the digital holographic interferometry system at 288.15 K, 298.15 K and 308.15 K over the mass fraction range of 0 ∼ 0.447 (0 ∼ 2 mol/L). The temperature and concentration influences of D were discussed and a semi-empirical correlation was built.

Vapor Cloud Behavior of Heavier-Than-Air Hydrocarbon Liquid Evaporating From a Microliter Volume Heated Well Cavity

Abstract Detailed characterization of the vapor cloud above a well or reservoir is not available in literature irrespective of its several practical importance. This study aims to understand the vapor cloud characteristics and vapor phase transport of a heavier-than-air vapor cloud evaporating from a heated microliter circular reservoir. Evaporation of a heavy hydrocarbon (cyclohexane) and a comparatively lighter fluid (ethanol) is studied. Digital holographic interferometry has been used for the characterization of vapor cloud. Gravimetric analysis is used for measurement of evaporation rate from the reservoir. A flat disk-shaped vapor cloud is observed in both heated and nonheated reservoir cases. This is attributed to the presence of radial outward natural convection. The evaporation rate is underpredicted by the diffusion model at a higher Grashof number, i.e., for well heating. Solutal convection dominates near the interface region and thermal convection effect increases in the region away from the liquid–vapor interface. The mole fraction profile depends on the relative strength of the thermal and solutal Grashof number. Thermal convection effect is stronger in lighter vapor of ethanol compared to that of cyclohexane. Overall, this study shows dominance of solutal convection on the vapor cloud characteristics above both heated and unheated reservoir.

High-frequency plasma spark discharge assessment in pulse-scanning digital holographic interferometry.

In this paper, we consider the method for observing and detecting of high-frequency pulsed plasma spark discharge by means of stroboscopic digital holographic interferometry to demonstrate the feasibility of the electron concentration assessment in nonthermal plasma. A spark discharge with a 5kHz frequency and 1250ns duration has been exited between two electrodes in atmospheric pressure. The sequence of holograms acquired due to the proper synchronization between plasma pulses, laser radiation, and the camera's frame grabbing that ensure the recording of the plasma pulses at different moments of time, and the temporal optical scanning of plasma pulse were realized. We also show that the phase difference contrast variation corresponds to the plasma's instant and indicates a change in the electron concentration in the discharge. The concentration of electrons at different moments of plasma existence has been estimated. The limitations of the proposed method are considered, and its applicability for the study of low-temperature pulsed plasma are discussed.

Convolutional and fourier neural networks for speckle denoising of wrapped phase in digital holographic interferometry

Owing to the unique non-Gaussian statistics and non-stationary properties, speckle denoising in digital holographic interferometry measurements is essential and challenging. Traditional Fourier-based frequency-domain filtering and deep-learning-based (DL-based) spatial-domain speckle denoising methods have realised significant achievements. However, these methods use different mechanisms and are applied separately. A deep learning-based algorithm combining the aforementioned methods is proposed, which simultaneously extracts features in the spatial and frequency domains via convolutional and Fourier neural networks (CFNN) for enhanced speckle denoising and minimising the phase unwrapping process error. Compared with the convolutional neural network (CNN), which provides spatial-domain features for the framework architecture to layer extraction operation, the Fourier neural operator (FNO) developed from traditional Fourier filtering provides the frequency-domain features. The differential gradient map of denoised wrapping is used as a loss function, further improving the smoothness of the denoised wrapped images and the subsequent phase-unwrapping performance. Compared to other algorithms, the proposed method achieves a relative improvement of 7.1% on the testing dataset and 5.0% on digital holographic deformation field experimental data. It can be extended to more fields of optical information processing, since it unifies the feature extraction and reconstruction in the spatial-domain and the frequency-domain in the same deep learning model.

Computational optical phase imaging: From digital holographic interferometry to intensity diffraction tomography

Computational optical phase imaging: From digital holographic interferometry to intensity diffraction tomography