Digital Holographic Reconstruction Research Articles

Single Beam Digital Holography Reconstruction: A Support-Enhanced Complex-Wavefront Filtering

Single Beam Digital Holography Reconstruction: A Support-Enhanced Complex-Wavefront Filtering

Single-beam digital holographic reconstruction: a phase-support enhanced complex wavefront on phase-only function for twin-image elimination.

In in-line digital holographic microscopy (DHM), twin-image artifacts pose a significant challenge, and reduction or complete elimination is essential for object reconstruction. To facilitate object reconstruction using a single hologram, significantly reduce inaccuracies, and avoid iterative processing, a digital holographic reconstruction algorithm called phase-support constraint on phase-only function (PCOF) is presented. In-line DHM simulations and tabletop experiments employing the sliding-window approach are used to compute the arithmetic mean and variance of the phase values in the reconstructed image. A support constraint mask, through variance thresholding, effectively enabled twin-image artifacts. Quantitative evaluations using metrics such as mean squared error, peak signal-to-noise ratio, and mean structural similarity index show PCOF's superior capability in eliminating twin-image artifacts and achieving high-fidelity reconstructions compared with conventional methods such as angular spectrum and iterative phase retrieval methods. PCOF stands as a promising approach to in-line digital holographic reconstruction, offering a robust solution to mitigate twin-image artifacts and enhance the fidelity of reconstructed objects.

3D polarization-interference holographic histology for wavelet-based differentiation of the polycrystalline component of biological tissues with different necrotic states. Forensic applications.

The interference-holographic method of phase scanning of fields of scattered laser radiation is proposed. The effectiveness of this method for the selection of variously dispersed components is demonstrated. This method made it possible to obtain polarization maps of biological tissues at a high level of depolarized background. The scale-selective analysis of such maps was used to determine necrotic changes in the optically anisotropic architectonics of biological tissues. Development and experimental approbation of layered phase polarimetry of repeatedly scattered fields in diffuse layers of biological tissues. Application of scale-selective processing of the found coordinate distributions of polarization states in various phase sections of object fields. Determination of criteria (markers) for histological differential diagnosis of the causes of necrotic changes in optical anisotropy of biological tissues. We used a synthesis of three instrumental and analytical methods. Polarization-interference registration of laser radiation scattered by a sample of biological tissue. Digital holographic reconstruction and layered phase scanning of distributions of complex amplitudes of the object field. Analytical determination of polarization maps of various phase cross-sections of repeatedly scattered radiation. Application of wavelet analysis of the distributions of polarization states in the phase plane of a single scattered component of an object field. Determination of criteria (markers) for differential diagnosis of necrotic changes in biological tissues with different morphological structure. Two cases are considered. The first case is the myocardium of those who died as a result of coronary heart disease and acute coronary insufficiency. The second case is lung tissue samples of deceased with bronchial asthma and fibrosis. A method of polarization-interference mapping of diffuse object fields of biological tissues has been developed and experimentally implemented. With the help of digital holographic reconstruction of the distributions of complex amplitudes, polarization maps in various phase sections of a diffuse object field are found. The wavelet analysis of azimuth and ellipticity distributions of polarization in the phase plane of a single scattered component of laser radiation is used. Scenarios for changing the amplitude of the wavelet coefficients for different scales of the scanning salt-like MHAT function are determined. Statistical moments of the first to fourth orders are determined for the distributions of the amplitudes of the wavelet coefficients of the azimuth maps and the ellipticity of polarization. As a result, diagnostic markers of necrotic changes in the myocardium and lung tissue were determined. The statistical criteria found are the basis for determining the accuracy of their differential diagnosis of various necrotic states of biological tissues. Necrotic changes caused by "coronary artery disease-acute coronary insufficiency" and "asthma-pulmonary fibrosis" were demonstrated by the method of wavelet differentiation with polarization interference with excellent accuracy.

Single-shot inline holography using a physics-aware diffusion model.

Among holographic imaging configurations, inline holography excels in its compact design and portability, making it the preferred choice for on-site or field applications with unique imaging requirements. However, effectively holographic reconstruction from a single-shot measurement remains a challenge. While several approaches have been proposed, our novel unsupervised algorithm, the physics-aware diffusion model for digital holographic reconstruction (PadDH), offers distinct advantages. By seamlessly integrating physical information with a pre-trained diffusion model, PadDH overcomes the need for a holographic training dataset and significantly reduces the number of parameters involved. Through comprehensive experiments using both synthetic and experimental data, we validate the capabilities of PadDH in reducing twin-image contamination and generating high-quality reconstructions. Our work represents significant advancements in unsupervised holographic imaging by harnessing the full potential of the pre-trained diffusion prior.

Numerical phase-detection autofocusing method for digital holography reconstruction processing

Digital holography requires precise phase analysis without wavefront aberration from defocusing errors. While the auto-contrast method is generally advantageous for refocusing, its accuracy suffers in low contrast conditions. Here, we suggest a simple phase analysis approach applying phase-detection autofocusing (PDAF) using a Fourier operation and the angular spectrum method to achieve fast and accurate refocusing even with high transmittance samples. The speed and sub-nano accuracy of the proposed method along with its quantitative autofocusing capability make it well suited for numerical digital holography reconstruction. Phase analysis with the PDAF technique has sensitivity to 0.1 nm changes in the focusing distance within the light source wavelength. In the reconstruction process, this method causes no wavefront distortion. The refocus calculation time using the focus calibration function obtained through the PDAF method is 0.012 s, enabling a real-time refocus correction of 83 fps in digital video holography. We performed refractive index measurements of Boro33 plates with high transmittance to interpret the physical meaning of the corrected focal distances and obtained a refractive index accuracy of 0.001. We then applied a real-time digital holographic measurement system and found that the PDAF technique removed dynamic phase fluctuation, an issue that cannot be solved by phase unwrapping.

Quality improvement to holographic microscopy reconstruction based on Kramers-Kronig relations and phase background fitting.

Spectrum aliasing in off-axis digital holography may result in poor image reconstruction quality. This study proposes a method to eliminate the zero-order spectrum and enhance image quality based on Kramers-Kronig relations and phase background fitting. The Kramers-Kronig relations are employed to derive the quotient of the complex wavefront of object and the reference beam, and phase background fitting is performed in a compensatory way to yield the object complex wavefront. The effectiveness of the proposed approach is validated via simulations and experiments. The results show significantly improved off-axis digital holographic microscopy reconstruction quality, making the proposed method a promising option for holographic microscopy imaging.

3D digital polarization-holographic wavelet histology in determining the duration of mechanical damage to the myocardium.

We aimed developing and experimentally validating methods for 3D scale-selective polarimetry of multiply scattered fields in diffuse myocardium layers for mechanical myocardial injury prescription histological differential diagnostics. We used the synthesis of diffuse object field polarization-interference registration and polarization-inhomogeneous field digital holographic reconstruction and layer-by-layer complex amplitudes distributions The method for selection single and diffuse object field multiply scattered components polarization maps is proposed. The conditions for eliminating the distorting influence of a depolarized background high level are found. On the basis of еру object field single scattered component polarization maps a large-scale selective wavelet analysis the criteria (markers) for mechanical myocardial injury different prescription diagnosis was determinate. Excellent accuracy mechanical injury myocardium necrotic changes with different duration using polarization-interference wavelet differentiation were achieved.

ADMM-inspired image reconstruction forterahertz off-axis digital holography.

Image reconstruction in off-axis terahertz digital holography is complicated due to the harsh recording conditions and the non-convexity form of the problem. In this paper, we propose an inverse problem-based reconstruction technique that jointly reconstructs the object field and the amplitude of the reference field. Regularization in the wavelet domain promotes a sparse object solution. A single objective function combining the data-fidelity and regularization terms is optimized with a dedicated algorithm based on an alternating direction method of multipliers framework. Each iteration alternates between two consecutive optimizations using projections operating on each solution and one soft thresholding operator applying to the object solution. The method is preceded by a windowing process to alleviate artifacts due to the mismatch between camera frame truncation and periodic boundary conditions assumed to implement convolution operators. Experiments demonstrate the effectiveness of the proposed method, in particular, improvements of reconstruction quality, compared to two other methods.

Autofocusing in off-axis digital Fresnel holography using S-th power weighted neighborhood correlation coefficient

Accurately determining the focus plane of the reconstructed image is crucial for obtaining high-quality reconstructed images in the process of digital hologram reconstruction. In this paper, a focusing evaluation function based on S-th power weighted neighborhood correlation coefficient (SPWNCC) is proposed to realize automatic focusing of the reconstructed image in off-axis digital Fresnel holography. The Fresnel transform method is utilized as the off-axis digital holographic reconstruction algorithm. Both the numerical simulation and optical experiment results are given to verify the validity of the proposed autofocusing method. The obtained focusing curve can maintain good unimodality and noise immunity performance over a large search range (approximately 800 mm). The proposed SPWNCC based focusing evaluation function has a certain guiding significance on the automatic focusing of off axis digital holographic reconstructed images of long distance recorded objects.

Image-to-image translation for improved digital holographic reconstruction based on a generative adversarial network learning framework

Based on the generative-discriminative model, a digital holographic reconstruction generative adversarial network is established (DHR-GAN), in which the objective function is optimized on the original model to evaluate the perceptual holographic image. Performing the goals of different optical characteristics, the experimental acquisition system of digital holographic images is designed and constructed for preparing the digital holographic reconstructed image dataset (DHR-dataset), which achieves supervised training and testing of DHR-GAN. From an extensive qualitative and quantitative comparative discussion, the reconstruction results are more stable, with higher contrast and sharper local details, demonstrating the effectiveness of the proposed network model.

Multi-particles detection in transformer oil based on lens-free digital holography and transport of intensity equation

Transformer oil quality plays an important role in health condition monitoring of a transformer. In order to better monitor the quality of transformer oil, a hybrid method combining lens-free digital holography and transport of intensity equation is proposed. We take a single-shot hologram of sample oil with a large field of view, and then reconstruct intensity images with different distances, which are necessary inputs of transport of intensity equation. After that, we can get relatively high-resolution image of oil. Via experiments, we compare the results from basic lens-free digital holography reconstruction and hybrid method reconstruction. The results show that, the hybrid method can effectively improve the image reconstruction accuracy and achieve a resolution of 2.67 μm across a large field-of-view of 60.35 mm2, which means that we can get high-resolution zoom-in oil image with more details and quantitative parameters in real time, which is a breakthrough for quick transformer oil testing.

Dual-wavelength off-axis digital holography inImageJ: toward real-time phase retrieval usingCUDA streams.

An ImageJ plug-in is developed to realize automatic real-time phase reconstruction for dual-wavelength digital holography (DH). This plug-in assembles the algorithms, including automatic phase reconstruction based on the division algorithm and post-processing. These algorithms are implemented and analyzed using a CPU and GPU, respectively. To hide the CPU-to-GPU data transfer latency, an optimization scheme using Compute Unified Device Architecture (CUDA) streams is proposed in ImageJ. Experimental results show that the proposed plug-in can perform faster reconstruction for dual-wavelength DH, resulting in frame rates up to 48fps even for one-megapixel digital holograms on a normal PC. In other words, the proposed plug-in can realize real-time phase reconstruction for dual-wavelength digital holographic videos.

Improved SNR and super-resolution reconstruction of multi-scale digital holography based on deep learning

Digital holography is one of the key technologies to obtain the wavefront information of three-dimensional objects, and obtaining high quality hologram is the primary condition. Due to the constraints of image sensors and the influence of experimental environment, the digital hologram obtained has the problems of speckle noise and low resolution. In order to overcome this constraint, a method based on deep learning is used to improve the hologram quality and fringe signal-to-noise ratio, and then high-quality reconstructed phase diagrams are obtained as data sets to realize end-to-end superresolution reconstruction. The results show that the obtained high quality hologram has better reconstruction effect, and the effect of speckle noise is suppressed. The performance of three loss functions in the training of the network is compared.

Real-time phase measurement of optical vortex via digital holography

Real-time phase measurement is of great value to study the evolution of optical vortex. However, it cannot be recorded in real time due to the limitation of the exposure time of the recording device in the experiment. Therefore, based on the temporal and spatial evolution correlation of the optical phase, a real-time phase measurement method of optical vortex generated by an acoustically induced fiber grating is proposed based on digital holographic reconstruction algorithm. First, a series of holograms are continuously recorded using a low frame rate CCD. Then, the evolution of optical vortex over time is translated into changes in transmission distance. Furthermore, the unrecorded vortex phase distributions are calculated using diffraction theory. By serializing these phase maps over time, the propagation and evolution of spiral phase structure of the vortex beam can be demonstrated in real time.

Fast automated quantitative phase reconstruction in digital holography with unsupervised deep learning

Digital holography can provide quantitative phase images related to the morphology and content of biological samples. To reconstruct an accurate phase image, several processes, such as phase unwrapping, focusing, and calculation of digital reference wave and numerical propagation, are essential. However, this process is time-consuming. We propose a model that performs phase reconstruction in one-step and two-step using an unsupervised image-to-image translation structure. The two-step reconstruction model translates the phase image, which is obtained by performing numerical propagation on the hologram, into an accurate phase image, whereas the one-step reconstruction model directly translates the hologram into an accurate phase image. The proposed model shows similar high-performance reconstruction to the supervised learning model used in many previous studies. However, since supervised learning is trained in strict pairs, many target domain data (accurate phase imagery) is required. Since the proposed model is trained by unsupervised learning, phase reconstruction can be performed with a small amount of target domain data. The proposed method can help to observe the morphology and movement of biological cells in real-time applications.

Fractional Fourier-transform filtering and reconstruction in off-axis digital holographic imaging.

An off-axis digital holographic reconstruction method with fractional Fourier transform domain filtering is proposed. The theoretical expression and analysis of the characteristics of fractional-transform-domain filtering are given. It is proven that the filtering in a lower fractional-order transform domain can utilize more high-frequency components than that in a conventional Fourier transform domain under the same size of filtering regions. In simulation and experiment, the results demonstrate that the reconstruction imaging resolution can be improved by filtering in the fractional Fourier transform domain. The presented fractional Fourier transform filtering reconstruction provides a novel (to our knowlede) optional way for off-axis holographic imaging.

Zero-order-removal off-axis digital holographic reconstruction using different beam-ratio holographic recordings

A method of the zero-order-removal off-axis digital holographic reconstruction by recording three holograms with different beam ratios is presented. The zero-order-removal hologram can be constituted using two fitting coefficients in the combination of three holograms, in which the three off-axis holograms with different intensity ratios of the object and reference beams are recorded via arbitrarily turning a half-wave plate. The validity of the zero-order removal of the resultant hologram is proved by expression. The filtering region for the zero-order-removal hologram can extend to the center of its spatial-frequency spectrum domain, which makes higher spatial frequencies on the positive first-order intercepted. In the experiments, the reconstructed amplitude and phase images demonstrate the effectiveness of this zero-order-removal approach and the improvement on imaging resolution.

End-to-End Phase Reconstruction of Digital Holography Based on Improved Residual Unet

End-to-End Phase Reconstruction of Digital Holography Based on Improved Residual Unet

Robust and fast auto-focusing using convolutional neural networks for off-axis digital holography

In this paper, we propose a robust and accurate estimation method for the distance required for digital holography (DH) reconstruction using convolutional neural networks (CNN) in off-axis DH (off-axis DH). This method applies adaptive spectral pooling to reflect distance-related optical characteristics and minimize information loss during the training phase. Simulations and experiments have confirmed that the proposed method is more robust and accurate than search-based or CNN-based distance estimation methods.

High quality of an absolute phase reconstruction for coherent digital holography with an enhanced anti-speckle deep neural unwrapping network.

It is always a challenge how to overcome speckle noise interference in the phase reconstruction for coherent digital holography (CDH) and its application, as this issue has not been solved well so far. In this paper, we are proposing an enhanced anti-speckle deep neural unwrapping network (E-ASDNUN) approach to achieve high quality of absolute phase reconstruction for CDH. The method designs a special network-based noise filter and embeds it into a deep neural unwrapping network to enhance anti-noise capacity in the image feature recognition and extraction process. The numerical simulation and experimental test on the phase unwrapping reconstruction and the image quality evaluation under the noise circumstances show that the E-ASDNUN approach is very effective against the speckle noise in realizing the high quality of absolute phase reconstruction. Meanwhile, it also demonstrates much better robustness than the typical U-net neural network and the traditional phase unwrapping algorithms in reconstructing high wrapping densities and high noise levels of phase images. The E-ASDNUN approach is also examined and confirmed by measuring the same phase object using a commercial white light interferometry as a reference. The result is perfectly consistent with that obtained by the E-ASDNUN approach.