Digital Holograms Research Articles

Speckle-noise filtering based on non-local mean sparse principal component analysis method

Speckle-noise filtering is an extensive and key process in coherent interferometric techniques to obtain important and accurate information from the recorded interferogram or fringe pattern. The speckle-noise inherent to these interferograms, recorded by digital image sensors in these optical techniques, is eliminated by an appropriate image processing method. Since the beginning and further development of these techniques, a wide variety of speckle noise filtering methods and techniques have been proposed. The present research work aims to exploit the performance of the non-local sparse principal component analysis (NLS-PCA) method for speckle noise reduction as applied to the interferometric fringe patterns obtained from digital holographic and digital speckle pattern interferometric techniques. The performance of the NLS-PCA is evaluated on numerical simulations using different types of speckle fringe patterns resulting in the method being highly effective in filtering the speckle noise and providing superior results evaluated in terms of peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), Edge preservation Index (EPI), and Equivalent Number of look (ENL), in comparison to widely known methods such as windowed Fourier transform method, Lee filter, Weiner filter. Furthermore, the proposed method maintains the finer details and preserves the fringe edges effectively. The performance of the NLS-PCA method is also demonstrated on experimental speckle fringe patterns and digital holograms.

Single capture bright field and off-axis digital holographic microscopy.

We report on a single capture approach for simultaneous incoherent bright field (BF) and laser-based quantitative phase imaging (QPI). Common-path digital holographic microscopy (DHM) is implemented in parallel with BF imaging within the optical path of a commercial optical microscope to achieve spatially multiplexed recording of white light images and digital off-axis holograms, which are subsequently numerically demultiplexed. The performance of the proposed multimodal concept is firstly determined by investigations on microspheres. Then, the application for label-free dual-mode QPI and BF imaging of living pancreatic tumor cells is demonstrated.

Zero-order term suppression in off-axis holography based on deep learning method

Zero-order image is the main source of noise in the hologram acquisition process. In this paper, a preprocessing method based on deep learning are proposed. Generative adversarial network is applied to suppress zero-order image of off-axis digital holograms. To simplify data preparation process, we propose a digital hologram corresponding database generation method. The simulated data is used to replace the experimental data in the training network. After testing, the experimental data can be processed into a hologram without zero-order image successfully. And the different types of images are reconstructed with high quality. This method shows high efficiency and excellent robustness.

Highly Stable Vibration Measurements by Common-path off-axis Digital Holography

Measurements of vibration and dynamic deformation with high resolution and accuracy by non-destructive techniques are of great interest in many branches of engineering. We report a non-destructive method based on digital holographic interferometry for high-precision micro-vibration measurement of a 3D object. Herein, a new and simple configuration of a common-path off-axis digital holographic system, with high mechanical stability and compact in size, is proposed. The simplicity, compactness, and high stability of the proposed setup are provided by employing a wedge plate in-line with the test object to create a clean reference beam. The object and reference beams propagate the same path, without the need for any specialized optical component or arrangement, and these beams allow to interfere on the faceplate of the image sensor to form a digital hologram. The feasibility of the proposed system is demonstrated by measuring the vibration of the objects driven by different excitation signals. A series of digital holograms of a vibrating object is recorded by the use of a high-speed image sensor. The result of the reconstruction of the recorded holograms is an array of complex numbers containing the complete amplitude and phase profile of the object, which in turn provides displacement and vibration information. Due to its simple geometry and significant outcomes, the proposed system could be established as a powerful tool for a wide range of optical imaging and measurement investigations.

Two-wavelength digital holography through fog

Interferometric detection enables the acquisition of the amplitude and phase of the optical field. By making use of the synthetic wavelength as a computational construct arising from digital processing of two off-axis digital holograms, it is possible to identify the shape of an object obscured by fog and further increase the imaging range due to the increased sensitivity in coherent detection. Experiments have been conducted inside a 27 m long fog tube filled with ultrasonically generated fog. We show the improved capabilities of synthetic phase imaging through fog and compare this technique with conventional active laser illumination imaging.

Off-Axis Holographic Interferometer with Ensemble Deep Learning for Biological Tissues Identification

This paper proposes a method with an off-axis interferometer and an ensemble deep learning (I-EDL) hologram-classifier to interpret noisy digital holograms captured from the tissues of flawed biological specimens. The holograms are captured by an interferometer, which serves as a digital holographic scanner to scan the tissue with 3D information. The method achieves a high success rate of 99.60% in identifying the specimens through the tissue holograms. It is found that the ensemble deep learning hologram-classifier can effectively adapt to optical aberration coming from dust on mirrors and optical lens aberrations such as the Airy-plaque-like rings out-turn from the lenses in the interferometer. The deep learning network effectively adapts to these irregularities during the training stage and performs well in the later recognition stage without prior optical background compensations. The method does not require an intact sample with a full outline shape of the specimens or the organs to understand the objects’ identities. It demonstrates a new paradigm in object identification by ensemble deep learning through a direct wavefront recognition technique.

Measurement of liquid refractive index by quantitative phase reconstruction of single frame dual-wavelength digital hologram

A measurement method of liquid’s refractive index by means of single-shot dual-wavelength off-axis digital holographic microscopy system and a microfluidic chip is presented. The refractive index of liquid filled in the microchannel of a microfluidic chip can be calculated directly with the unwrapped phase map obtained at the synthetized wavelength, without requirement of knowing another liquid’s refractive index as a measurement reference. Moreover, it also avoids the influence of the accuracy of the known refractive index on the refractive index to be measured. The refractive indices of salt solutions and sucrose solutions in the several concentrations are achieved in the experiments, which verifies that the measurement of liquid’s refractive index is accurate and reliable. With the merit of accurate measurement of liquid’s refractive index based on the one-frame dual-wavelength hologram, it is expected that this approach will be employed to detect in real-time the fluid’s parameters related to its refractive index.

H-FISTA: a hierarchical algorithm for phase retrieval with application to pulsar dynamic spectra

ABSTRACT A pulsar dynamic spectrum is an inline digital hologram of the interstellar medium; it encodes information on the propagation paths by which signals have travelled from source to telescope. To decode the hologram, it is necessary to ‘retrieve’ the phases of the wavefield from intensity measurements, which directly gauge only the field modulus, by imposing additional constraints on the model. We present a new method for phase retrieval in the context of pulsar spectroscopy. Our method makes use of the Fast Iterative Shrinkage Thresholding Algorithm (FISTA) to obtain sparse models of the wavefield in a hierarchical approach with progressively increasing depth. Once the tail of the noise distribution is reached the hierarchy terminates with a final, unregularized optimization. The result is a fully dense model of the complex wavefield that permits the discovery of faint signals by appropriate averaging. We illustrate the performance of our method on synthetic test cases and on real data. Our algorithm, which we call H-FISTA, is implemented in the python programming language and is freely available.

An interference iterative reconstruction algorithm based on a single off-axis digital hologram

Compared with in-line digital holography, off-axis digital holography can be used to image more kinds of objects without the limitation of weak phase and sparsity, but the resolution is lower. In this paper, we put forward a high-resolution iterative reconstruction algorithm for off-axis holography which requires only the recording of a single off-axis hologram. Interference and inverse interference processes are used to extract the object wave from the off-axis hologram. A frequency domain filter with increasing radius is used to improve the competitive advantage of low frequency information in the early iterations, while retaining the high frequency information. The constraints of total variation and positive absorption are implemented at the object plane to enhance the robustness of the algorithm. Numerical simulations and experiments show that the resolution of our method is higher than traditional off-axis digital holography. In addition, it is suitable for phase image when the object has a strong phase fluctuation, as well as for imaging dynamical objects.

Dynamic Polarization Holographic Projection Enabled by a Scattering Material-Based Reconfigurable Hologram

Polarization holography generally depends on polarization-sensitive materials to reconstruct vectorial images, hindering the reconfigurability of the polarization hologram. As a result, it remains challenging to realize dynamic polarization holography that requires the hologram to integrate reconfigurability with vectorial wavefront control. Here, we demonstrate dynamic vectorial holographic projections by using a scattering material-based reconfigurable hologram. The hologram is composed of a scattering material and a digital hologram. The scattering material made of polyvinylidene fluoride nanofibers transforms the incident light into a speckle field with space-variant polarization states. Then, wavefront control of the hologram enables reconstruction of arbitrary vectorial images with reconfigurability. The scattering-enabled polarization holographic technique enables encoding multiple images with six polarization channels that can be flexibly switched. Furthermore, dynamic polarization-multiplexed image projection at a frame rate of up to 22, 270 fps can be achieved. A polarization-dependent 3D multiplane projection is also demonstrated. Our approach offers a unique avenue to dynamic holographic techniques with polarization multiplexing capability.

An optical-digital method of noise suppression in digital holography

Digital holography is widely used for the fast and accurate reconstruction of objects and three-dimensional (3D)-scene characteristics, such as intensity, depth locations, density, temperature, refractive index, etc. There are various types of noise in digital holography, including speckle noise, zero order and twin image, camera shot noise, and fixed-pattern noise. Therefore, additional hologram processing is required to obtain a high-quality reconstructed image. This paper proposes an optical-digital method of noise suppression in digital holography. First, a set of uncorrelated holograms is registered. The reconstructed images are treated as a 3D array, to which a 3D filter is then applied. A 3D median filter is used in this study; however, other 3D filters based on weighted averages can also be utilized. The proposed method is verified using computer-generated and optically registered digital Fresnel holograms. Even with a small number of images, the method significantly improves the quality: the normalized standard deviation was decreased by up to 3–10 times and the speckle contrast was reduced by more than six times. Registration of only ten holograms is sufficient to obtain a quality close to the maximum. The flexibility of the proposed method allows the ratio of the reconstruction quality to be varied according to the processing time.

Digital holographic imaging of thermal signatures and its use in inhomogeneity identification

Thermal diffusion results in spatiotemporally evolving temperature distribution in material mediums. In dielectric mediums it will also lead to change in local refractive index. Digital holographic interferometry can be used for high contrast quantitative phase imaging of spatially and temporally evolving refractive index variations in dielectric mediums, arising due to temperature changes. Phase retrieved from numerically processed digital holograms recorded at different time instances can be compared to obtain this information. Since the thermal conductivity varies for materials, the thermal diffusion and hence the refractive index across them will also vary. Time varying optical path length distributions of the test sample under thermal stress, obtained from the phase maps retrieved from the recorded holograms, can be used to obtain this information. This leads to detection of regions having different thermal conductivities and hence identification of inhomogeneities in the test sample. Lens less Fourier transform digital holographic interferometry is a perfect tool to quantify optical path length distributions with nanometre accuracy to quantify spatiotemporally evolving refractive index distributions. In this technique, a single Fourier transform of the recorded hologram yields the spatial distribution of object phase, making it ideal for real time imaging and applications. Here we describe our efforts in the development lens less Fourier transform digital holographic interferometric technique for the imaging and quantification of spatiotemporally evolving refractive index distributions in test samples under thermal stress and its application in detection of surface and sub-surface inhomogeneities.

Refractive index profile measurement of planar optical waveguides based on the near-field technique and digital holography

• A new method is proposed to measure the refractive index profile of planar optical waveguides. • A measurement system is established by integrating the near-field setup and the modified Mach-Zehnder interferometer. • The refractive index profile is quantitatively obtained from two phase difference distributions. • The accuracy of the proposed method is up to 1 × 10 −3 . A method based on the near-field technique and digital holography is proposed to measure the refractive index profile of planar optical waveguides. A measurement system is established by integrating the near-field setup and the modified Mach-Zehnder interferometer. The near-field intensity profile of optical waveguides is recorded by CCD to get the primary refractive index profile. The primary phase difference distribution is calculated based on the relationship between the phase and refractive index. The digital hologram is acquired by the modified Mach-Zehnder interferometer to extract the real phase difference distribution using the hybrid reconstruction algorithm. According to the relationship between the primary and real phase difference distributions, the refractive index profile is quantitatively obtained. To evaluate the proposed method, we measured the refractive index profiles of multimode and single-mode fibers and compared with S14 based on refracted near-field method and SHR-1802 based on digital holography. The proposed method has a measurement accuracy of up to 1 × 10 -3 and is suitable for measuring the positive refractive index region of waveguides.

Two-Step Converging Spherical Wave Diffracted at a Circular Aperture of Digital In-Line Holography.

The aspheric light emitted from a pinhole restrains the reconstruction quality of a digital in-line hologram. Herein, the Fresnel-diffracted spot from the first step converging spherical wave diffracted at a rough circular aperture is collimated and expanded to generate an even plane wave, which is converged again by an objective lens and matching a minimum aperture while the central spot is varying from light to dark. We observed that the collected background hologram is filled with a round spot with high contrast as an ideal spherical wave. The resolution board and biology experimental results demonstrated a distinctively reconstructed image without any image processing in a single exposure. The adjustable field of view and magnification, single exposure, and noncontact make it suitable for an online microscope.

Genetic algorithm based image reconstruction applying the digital holography process with the Discrete Orthonormal Stockwell Transform technique for diagnosis of COVID-19

World Health Organization has described the real-time reverse transcription-polymerase chain reaction test method for the diagnosis of the novel coronavirus disease (COVID-19). However, the limited number of test kits, the long-term results of the tests, the high probability of the disease spreading during the test and imaging without focused images necessitate the use of alternative diagnostic methods such as chest X-ray (CXR) imaging. The storage of data obtained for the diagnosis of the disease also poses a major problem. This causes misdiagnosis and delays treatment. In this work, we propose a hybrid 3D reconstruction method of CXR images (CXRI) to detect coronavirus pneumonia and prevent misdiagnosis on CXRI. We used the digital holography technique (DHT) for obtaining a priori information of CXRI stored in created digital hologram (CDH). In this way, the elimination of the storage problem that requires high space was revealed. In addition, Discrete Orthonormal S-Transform (DOST) is applied to the reconstructed CDH image obtained by using DHT. This method is called CDH_DHT_DOST. A multiresolution spatial-frequency representation of the lung images that belong to healthy people and diseased people with the COVID-19 virus is obtained by using the CDH_DHT_DOST. Moreover, the genetic algorithm (GA) is adopted for the reconstruction process for optimization of the CDH image and then DOST is applied. This hybrid method is called CDH_GA_DOST. Finally, we compare the results obtained from CDH_DHT_DOST and CDH_GA_DOST. The results show the feasibility of reconstructing CXRI with CDH_GA_DOST. The proposed method holds promises to meet demands for the detection of the COVID-19 virus.

Deep convolutional neural network for binary regression of three-dimensional objects using information retrieved from digital Fresnel holograms

Deep convolutional neural network for binary regression of three-dimensional objects using information retrieved from digital Fresnel holograms

Single-Pixel Compressive Digital Holographic Encryption System Based on Circular Harmonic Key and Parallel Phase Shifting Digital Holography

An encryption system that combines compressive sensing (CS) and two-step parallel phase shifting digital holography (PPSDH) using double random phase encoding (DRPE) is presented in this paper. The two-step PPSDH is a linear inline holographic scheme and is much suitable for encrypting the 2D/3D information in a single exposure. The distribution of random phase mask (RPM) in the DRPE is implemented using circular harmonic key which increases the security of the encryption process. In this system, the keys used to encrypt are spatial positions of the planes, wavelength, and rotation of the circular harmonics in RPMs, and CS acts as an additional key that makes the system more secure than the conventional optical encryption methods. At the transmission end, two-step PPSDH is applied to encrypt the object information in single hologram. The digital mirror device (DMD) is placed between the object and a single-pixel detector for acquiring fewer hologram measurements. At the receiver end, the single digital hologram is numerically recovered by using a CS optimization problem. The original complex object field is decrypted from the CS recovered holograms by the inversion of two-step PPSDH process with the help of the correct keys. The numerical simulations are presented for complex 2D and 3D objects to test the feasibility of the proposed encryption and decryption system. The proposed method carried out intensity and phase reconstruction of the original object field using single-pixel compressive imaging. The computer simulation results demonstrated that the encrypted information is highly secured with the rotation of the circular harmonic key. The sensitivity of the decrypted intensity and phase images is also studied with variations of the encrypted keys. The obtained results show that the proposed encryption scheme is feasible and has better security performance and robustness.

Learning-based complex field recovery from digital hologram with various depth objects.

In this paper, we investigate a learning-based complex field recovery technique of an object from its digital hologram. Most of the previous learning-based approaches first propagate the captured hologram to the object plane and then suppress the DC and conjugate noise in the reconstruction. To the contrary, the proposed technique utilizes a deep learning network to extract the object complex field in the hologram plane directly, making it robust to the object depth variations and well suited for three-dimensional objects. Unlike the previous approaches which concentrate on transparent biological samples having near-uniform amplitude, the proposed technique is applied to more general objects which have large amplitude variations. The proposed technique is verified by numerical simulations and optical experiments, demonstrating its feasibility.

Deep-learning based reconstruction in optical scanning holography

Optical scanning holography (OSH) can be used to record holograms of large three-dimensional (3-D) objects, based on a two-dimensional (2-D) optical scan. For semi-transparent objects, diffraction waves from all the sections can be recorded in the hologram. Numerical reconstruction of a 3-D volumetric image from an optical scanned hologram is a difficult task. The main problems are the intensive computational load, and the heavy blurring of each reconstructed section with the defocused noise from other sections. In this paper, we propose a deep-learning network for high quality image reconstruction from the optical scanned holograms. Within the framework, a U-net structure is adopted to learn the mapping between a collection of holograms, and their reconstructed volumetric images. We use a two-pupil optical heterodyne scanning system to obtain the training data where a five-fold cross validation method is used to prevent from overfitting and produce enough images in the dataset. The deep-learning based OSH can eliminate the defocus noise and generate high quality reconstruction results from an unknown hologram. Our proposed method is significantly faster than conventional OSH reconstruction algorithms, and hence suitable for processing large holograms that are captured by OSH. The feasibility of our approach is demonstrated with numerical simulations and optical experiments. The deep-learning reconstruction method proposed in the present paper is also applicable to other digital holograms obtained from conventional digital holographic systems.

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.