Experimental Holograms Research Articles

Near-field electron ptychography using full-field structured illumination.

A new configuration for near-field ptychography using a full-field illumination with a structured electron beam is proposed. A structured electron beam illuminating the entire field of view is scanned over the specimen, and a series of in-line holograms formed in the near-field region below the specimen are collected. The structured beam is generated by a conductive film with random openings, which ensures high stability and coherence of the beam. Observation in the near-field region reduces the beam concentration that occurs in the far-field region, which contributes to accurate recording of the beam intensity with a finite dynamic range of the detectors. The use of full-field illumination prevents the accumulation of errors caused by concatenating the local structures, which is the method used in conventional reconstruction. Since all holograms are obtained from the entire field of view, they have uniform multiplicity in terms of specimen information within the field of view. This contributes to robust and efficient reconstruction for a large field of view. The proposed method was tested using both simulated and experimental holograms. For the simulated holograms, the reconstruction of the specimen transmission function was achieved with an error less than 1/3485 of the wavelength. The method was further validated using experimental holograms obtained from MgO particles. The reconstructed phase transmission function of the specimen was consistent with the specimen structure and was equivalent to a mean inner potential of V on the MgO particle, which is in close agreement with previously reported values.

Speckle Reduction in Digital Holography by Fast Logistic Adaptive Non-Local Means Filtering

Digital holography is a promising imaging technology. However, there is speckle noise in the reconstructed image of a digital hologram. Speckle degrades the quality of the reconstructed image. Suppression of speckle noise is a challenging problem in digital holography. A novel method is proposed to reduce speckle by a fast logistic adaptive non-local means (LA-NLM) algorithm. In the proposed method, the logistic function is incorporated into the weight calculation of the NLM algorithm to account for multiplicative speckle noise. Filtering parameters are dynamically adjusted according to the statistical property of speckle in the reconstructed image. To enhance computational efficiency, the proposed algorithm takes advantage of the integral image technique to speed up the calculation of the similarity between image patches. Simulated and experimental digital holograms are obtained to verify the proposed method. The results show that the speckle noise is effectively suppressed in digital holography. The proposed method is efficient and feasible, and can be applied to such fields as three-dimensional display, holographic measurement, and medical diagnosis.

A digital twin approach for experimental acoustic hologram optimization

The need for the accurate generation of acoustic holograms has increased with the prevalence of the use of acoustophoresis methods such as ultrasonic haptic sensation, acoustic levitation, and displays. However, experimental results have shown that the actual acoustic field may differ from the simulated field owing to uncertainties in the transducer position, power and phase, or from nonlinearity and inhomogeneity in the field. Traditional methods for experimentally optimizing acoustic holograms require prior calibration and do not scale with the number of variables. Here, we propose a digital twin approach that combines feedback from experimental measurements (such as a microphone and an optical camera) in the physical setup with numerically obtained derivatives of the loss function, using automatic differentiation, to optimize the loss function. This approach is number of transducers times faster and more efficient than the classical finite difference approach, making it beneficial for various applications such as acoustophoretic volumetric displays, ultrasonic haptic sensations, and focused ultrasound therapy.

Open-access database for digital lensless holographic microscopy and its application on the improvement of deep-learning-based autofocusing models.

Among modern optical microscopy techniques, digital lensless holographic microscopy (DLHM) is one of the simplest label-free coherent imaging approaches. However, the hardware simplicity provided by the lensless configuration is often offset by the demanding computational postprocessing required to match the retrieved sample information to the user's expectations. A promising avenue to simplify this stage is the integration of artificial intelligence and machine learning (ML) solutions into the DLHM workflow. The biggest challenge to do so is the preparation of an extensive and high-quality experimental dataset of curated DLHM recordings to train ML models. In this work, a diverse, open-access dataset of DLHM recordings is presented as support for future research, contributing to the data needs of the applied research community. The database comprises 11,760 experimental DLHM holograms of bio and non-bio samples with diversity on the main recording parameters of the DLHM architecture. The database is divided into two datasets of 10 independent imaged samples. The first group, named multi-wavelength dataset, includes 8160 holograms and was recorded using laser diodes emitting at 654nm, 510nm, and 405nm; the second group, named single-wavelength dataset, is composed of 3600 recordings and was acquired using a 633nm He-Ne laser. All the experimental parameters related to the dataset acquisition, preparation, and calibration are described in this paper. The advantages of this large dataset are validated by re-training an existing autofocusing model for DLHM and as the training set for a simpler architecture that achieves comparable performance, proving its feasibility for improving existing ML-based models and the development of new ones.

Photoelectron holographic evidence for the incorporation site of Se and suppressed atomic displacement of the conducting layer of La(O,F)BiSSe

La(O,F)BiS2-x Se x is a layered material that is considered to be a candidate exotic superconductor as well as a promising thermoelectrical material. We performed soft X-ray photoelectron holography to study the Se incorporation site and the local atomic arrangement of the conducting layer. A comparison of the experimental holograms with the simulated holograms indicates that Se atoms preferentially occupy the S sites in the conducting Bi–S plane of La(O,F)BiS2. A comparison between the state-of-the-art holographic reconstructions of La(O,F)BiSSe and La(O,F)BiS2 suggests that Se substitution suppresses the displacement of S atoms in La(O,F)BiS2. These results provide photoelectron holographic evidence for the Se incorporation site and the Se-induced suppression of in-plane disorder.

Self-supervised learning of hologram reconstruction using physics consistency

Existing applications of deep learning in computational imaging and microscopy mostly depend on supervised learning, requiring large-scale, diverse and labelled training data. The acquisition and preparation of such training image datasets is often laborious and costly, leading to limited generalization to new sample types. Here we report a self-supervised learning model, termed GedankenNet, that eliminates the need for labelled or experimental training data, and demonstrate its effectiveness and superior generalization on hologram reconstruction tasks. Without prior knowledge about the sample types, the self-supervised learning model was trained using a physics-consistency loss and artificial random images synthetically generated without any experiments or resemblance to real-world samples. After its self-supervised training, GedankenNet successfully generalized to experimental holograms of unseen biological samples, reconstructing the phase and amplitude images of different types of object using experimentally acquired holograms. Without access to experimental data, knowledge of real samples or their spatial features, GedankenNet achieved complex-valued image reconstructions consistent with the wave equation in free space. The GedankenNet framework also shows resilience to random, unknown perturbations in the physical forward model, including changes in the hologram distances, pixel size and illumination wavelength. This self-supervised learning of image reconstruction creates new opportunities for solving inverse problems in holography, microscopy and computational imaging.

Photon-starved snapshot holography

Digital holography (DH) is a powerful imaging modality that is capable of capturing the object wavefront information, making it very valuable for diverse scientific research applications. Generally, it requires ample illumination to enable good fringe visibility and a sufficient signal-to-noise ratio. As such, in situations such as probing live cells with minimal light interaction and high-speed volumetric tracking in flow cytometry, the holograms generated with a limited photon budget suffer from poor pattern visibility. While it is possible to make use of photon-counting detectors to improve the hologram quality, the long recording procedure coupled with the need for mechanical scanning means that real-time extremely low-light holographic imaging remains a formidable challenge. Here, we develop a snapshot DH that can operate at an ultra-low photon level (less than one photon per pixel). This is achieved by leveraging a quanta image sensor to capture a stack of binary holographic frames and then computationally reconstructing the wavefront through integrating the mathematical imaging model and the data-driven processing, an approach that we termed PSHoloNet. The robustness and versatility of our DH system are demonstrated on both synthetic and experimental holograms with two common DH tasks, namely particle volumetric reconstruction and phase imaging. Our results demonstrate that it is possible to expand DH to the photon-starved regime, and our method will enable more advanced holography applications in various scientific imaging systems.

Photoelectron Holography Study of La(O,F)BiS2

We performed photoelectron and Auger-electron holography to study the local structure of La(O,F)BiS2. We found that the previously reported surface-dominant Bi 4f component exhibits a hologram pattern derived from the forward focusing peaks. This provides spectroscopic evidence of the existence of two bulk Bi chemical sites. A comparison of the experimental holograms with the simulated holograms using the tetragonal and charge density wave (CDW) models, as well as with a monoclinic structure model proposed for the high-pressure phase exhibiting the highest superconducting transition temperature among BiS2-based superconductors, showed that the monoclinic structure model does not reproduce the experimental hologram. This suggests that it may not be a dominant local structure for as-grown La(O,F)BiS2. The experimental hologram of the F KLL Auger electron is similar to that of the O KLL, providing direct evidence that F atoms are incorporated into the O sites. These results are valuable for understanding the physical properties of La(O,F)BiS2.

Digital holographic microscopy applied to 3D computer microvision by using deep neural networks

Deep neural networks are increasingly applied in many branches of applied science such as computer vision and image processing by increasing performances of instruments. Different deep architectures such as convolutional neural networks or Vision Transformers can be used in advanced coherent imaging techniques such as digital holography to extract various metrics such as autofocusing reconstruction distance or 3D position determination in order to target automated microscopy or real-time phase image restitution. Deep neural networks can be trained with both datasets simulated and experimental holograms, by transfer learning. Overall, the application of deep neural networks in digital holographic microscopy and 3D computer micro-vision has the potential to significantly improve the robustness and processing speed of holograms to infer and control a 3D position for applications in micro-robotics.

PyDHM: A Python library for applications in digital holographic microscopy.

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.

Programmable Aperture Using a Digital Micromirror Device for In-Line Holographic Microscopy

We present the novel use of a digital micromirror device (DMD) as a digitally adjustable and programmable aperture for lens-free digital in-line holographic microscopy (LFDIHMY). The proposed micro-electro-mechanical system (MEMS) based DMD aperture can be used as a compact and inexpensive replacement for existing mechanical based aperture control methods required for many resolution enhancement methods. Simulation of the DMD aperture in an in-line holography setup is presented using a general DMD model from literature. A hardware prototype was successfully implemented and demonstrated. The resolution of both simulated and experimental holograms recorded with a conventional pinhole aperture and the proposed DMD aperture are compared using a 1951 USAF resolution test target. We show that a resolution of 2.46 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathrm { \mu {\textbf {m}}}$ </tex-math></inline-formula> , close to the sensor pixel limit, can be obtained with each aperture. This demonstrates that a DMD can be used as a programmable replacement for a pinhole aperture.

Fast autofocusing using tiny transformer networks for digital holographic microscopy.

The numerical wavefront backpropagation principle of digital holography confers unique extended focus capabilities, without mechanical displacements along z-axis. However, the determination of the correct focusing distance is a non-trivial and time consuming issue. A deep learning (DL) solution is proposed to cast the autofocusing as a regression problem and tested over both experimental and simulated holograms. Single wavelength digital holograms were recorded by a Digital Holographic Microscope (DHM) with a 10x microscope objective from a patterned target moving in 3D over an axial range of 92 μm. Tiny DL models are proposed and compared such as a tiny Vision Transformer (TViT), tiny VGG16 (TVGG) and a tiny Swin-Transfomer (TSwinT). The proposed tiny networks are compared with their original versions (ViT/B16, VGG16 and Swin-Transformer Tiny) and the main neural networks used in digital holography such as LeNet and AlexNet. The experiments show that the predicted focusing distance ZRPred is accurately inferred with an accuracy of 1.2 μm in average in comparison with the DHM depth of field of 15 µm. Numerical simulations show that all tiny models give the ZRPred with an error below 0.3 µm. Such a prospect would significantly improve the current capabilities of computer vision position sensing in applications such as 3D microscopy for life sciences or micro-robotics. Moreover, all models reach an inference time on CPU, inferior to 25 ms per inference. In terms of occlusions, TViT based on its Transformer architecture is the most robust.

Determination of site occupancy of boron in 6H–SiC by multiple-wavelength neutron holography

The local structure around boron doped in 6H-type silicon carbide (SiC) was investigated using neutron holography. Three-dimensional atomic images reconstructed from multiple-wavelength holograms revealed the boron substitution for both silicon and carbon. To determine boron locations accurately, we calculated holograms with varying occupancies of six different sites and fit image intensities with those obtained from experimental holograms by the steepest descent method. As a result, it was found that boron atoms were selectively located at the Si–C-cubic site layer. Furthermore, boundaries right above the boron locations were suggested from the absence of atomic images in the upper region of reconstruction.

Efficient coding of experimental holograms using speckle denoising

Lossy compression for Experimental Holograms (EH) and Computer-Generated Holograms (CGH) using standardized coding solutions is a highly efficient process provided that these solutions can be applied to the object plane. This compression efficiency reveals to be more relevant in CGH. Speckle noise mainly affects reconstructed EH, and to less extent reconstructed CGH. In the current work, the reduction of speckle noise of EH is proposed to improve the coding efficiency of the hologram compression scheme. The compression scheme defines a base layer where a 2D version of the object is coded with an image codec standard. When speckle noise reduction is performed before any compression, efficient compression is obtained for both CGH and EH. Since speckle noise reduction is performed only on amplitude data, without affecting the phase information of the reconstructed hologram, it is still possible to render 3D features such as depth map, multi-view or to recover holographic interference patterns for further 3D visualization.

High-efficiency holographic metacoder for optical masquerade.

Optical masquerades are a low-cost camouflage strategy that avoids the hidden objects to be recognized despite being detected. Here, we demonstrate an optical holography-based masquerade that could encode the camouflaged object ("bomb") into another uncorrelated phase object ("dog") by using transmissive dielectric metasurfaces with the total efficiency as high as 78% at visible wavelengths. The phase modulation in the encoded "dog" is realized by changing the inplane orientation of nanostructures. Illuminated by the circularly polarized light, the experimental hologram fabricated by using electron-beam lithography exhibits only the "dog" pattern when observing the surface of sample. To recover the hidden "bomb," one can observe the holographic image reconstructed at the Fresnel region, which works at the broadband spectrum from 540 nm to 680 nm. Such a technique might find potential applications in information security and military affairs.

Experimental study of thermal convection in dry air by holographic interferometry method

The convection of humid and dry air was studied experimentally. The observed fluid convection involves two mechanisms of heat and mass transfer: thermal convection produced by temperature gradient and concentration convection occurred due to the inhomogeneous vapor distributions induced by evaporation and condensation of water. The convection stability of humid air was described in terms of two (thermal and concentration) Rayleigh numbers. Laboratory experiments were performed using holographic interferometry and thermocouple techniques. Experimental holograms were processed numerically in order to calculate the space distribution of the refractive index in relation to temperature and vapor concentration. The results show that the difference between moist convection and dry convection can be measured even in the absence of evaporation and condensation. The experimental interferograms for dry air (intentionally dried up to 4 % relative humidity) are given. The justification of this research requires an additional quantitative comparison with the measurements obtained for humid air (with 100 % relative humidity) undergoing the first-order phase transition of the “gas-liquid” type.

Twin-image suppression in digital in-line holography based on wave-front filtering

Digital holography is an imaging process able to recreate three-dimensional representations of objects from recording pattern interference among distinct waves. The in-line configuration setup is a variant considered the simplest physical implementation, providing a feasible manner for acquisition and the same time higher resolution for free-living microscopy imaging using a single illumination system. However, the well-known twin-image problem is bounded to the technique, since there is no separation among reference and objects beams in this configuration. As a result, computational numerical diffraction routines present the twin-image effect intrinsically, imposing several difficulties in terms of post-processing requirements. In this context, this paper aims to present a numerical approach able to provide consistent suppression of twin-image problem for in-line holography, during the numerical diffraction procedure for phase retrieval, combining image subtraction and edge detection techniques. The proposed solution was implemented in Python language, and metrics defined to assess it were both qualitative and quantitative, based on edge detection and some image comparison metrics. The obtained results of the proposed approach present a significant reduction in the twin-image artifacts in the reconstructions of both experimental and simulated holograms, considering a spherical reference wave, regardless of the shapes and sizes of objects.

Digital lensless holographic microscopy: numerical simulation and reconstruction with ImageJ.

The description and validation of an ImageJ open-source plugin to numerically simulate and reconstruct digital lensless holographic microscopy (DLHM) holograms are presented. Two modules compose the presented plugin: the simulation module implements a discrete version of the Rayleigh-Somerfield diffraction formula, which allows the user to directly build a simulated hologram from a known phase and/or amplitude object by just introducing the geometry parameters of the simulated setup; the plugin's reconstruction module implements a discrete version of the Kirchhoff-Helmholtz diffraction integral, thus allowing the user to reconstruct DLHM holograms by introducing the parameters of the acquisition setup and the desired reconstruction distance. The plugin offers the two said modules within the robust environment provided by a complete set of built-in tools for image processing available in ImageJ. While the simulation module has been validated through the evaluation of the forecasted lateral resolution of a DLHM setup in terms of the numerical aperture, the reconstruction module is tested by means of reconstructing experimental DLHM holograms of biological samples.

PyHoLo software, a new tool for electron hologram analysis and magnetic investigation

A new tool, PyHoLo software, was developed to help automate the process of determining magnetic field B from a series of holograms, registered at different environmental conditions (e.g. for upside and downside orientations of a sample in a specimen holder). This procedure involves translating single holograms into phase shift of electron wave, but also alignment of holograms (i.e. shift, rotation, magnification) and finally calculation of magnetic field in an area selected for investigation. PyHoLo gives functionality for performing all those steps without having to refer to different programs and tools. The workflow and results of this procedure are discussed using as an example sample of FeCuSiB. Program summaryProgram title: PyHoLoCPC Library link to program files:http://dx.doi.org/10.17632/n4rgzbnbpw.1Licensing provisions: GNU General Public License 3Programming language: PythonNature of problem: (1) Determination of electric and magnetic fields in a sample on the basis of electron holograms, registered on transmission electron microscope during off-axis holography experiment. (2) Alignment of electron holograms acquired for different sample orientations and at different external conditions.Solution method: PyHoLo adapts procedures for all steps required to translate experimental holograms into electric and magnetic signals, i.e. restoration of total phase shift, alignment of electron holograms (shift, rotation, magnification, warping), phase separation. There is no need to refer to different tools and programs.Additional comments: PyHoLo requires Python 3.6 or newer and a number of external libraries to work (see README or manual for details). PyHoLo is a constant work in progress, developed by only one person, so it might be a little rough around the edges.

Simulation-Trained Sparse Coding for High-Precision Phase Imaging in Low-Dose Electron Holography.

We broaden the applicability of sparse coding, a machine learning method, to low-dose electron holography by using simulated holograms for learning and validation processes. The holograms, with shot noise, are prepared to generate a model, or a dictionary, that includes basic features representing interference fringes. The dictionary is applied to sparse representations of other simulated holograms with various signal-to-noise ratios (SNRs). Results demonstrate that this approach successfully removes noise for holograms with an extremely small SNR of 0.10, and that the denoised holograms provide the accurate phase distribution. Furthermore, this study demonstrates that the dictionary learned from the simulated holograms can be applied to denoising of experimental holograms of a p-n junction specimen recorded with different exposure times. The results indicate that the simulation-trained sparse coding is suitable for use over a wide range of imaging conditions, in particular for observing electron beam-sensitive materials.