Phase Reconstruction Method Research Articles

Accurate reconstruction of turbine blade point cloud and multiple point cloud registration based on structured light

In recent years, the industry has increasingly adopted vision-guided laser processing techniques to machine cooling holes in turbine blades, placing higher demands on the accuracy of visual measurement. Therefore, to meet the need for high-precision measurement of the three-dimensional shape of turbine blades in the context of laser processing of film cooling holes, this paper proposes a structured light-based method for precise reconstruction of turbine blade point clouds and multi-point cloud registration. Specifically, to improve the accuracy of point cloud reconstruction, an improved phase matching and reconstruction method based on epipolar constraints is proposed. To enhance the accuracy and stability of multi-point cloud registration, a weighted iterative closest point (ICP) method combined with a mark point registration strategy is proposed, optimizing the stability of the ICP algorithm and achieving high-quality multiple point cloud stitching. Experimental results validate the effectiveness and accuracy of the proposed method.

Phase reconstruction by phase shift estimation with reliable parameter minimization

Abstract Achieving a balance between accuracy and speed in phase reconstruction is a challenging problem. In phase-shifting interferometry, increasing the speed by reducing the number of phase shifts while maintaining high accuracy is highly desirable. We propose an accurate and efficient two-step phase reconstruction method utilizing random phase shift. This method directly estimates the phase shift through reliable parameter minimization, allowing for easy and precise phase reconstruction. Simulations and experiments demonstrate the superior performance of our method across various scenarios, outperforming well-known two-step phase-shifting algorithms. We expect this paper to provide a general and powerful tool for phase reconstruction.

Phase recovery from Fresnel incoherent correlation holography using differential Zernike fitting.

Fresnel incoherent correlation holography (FINCH) was created to improve imaging resolution and 3D imaging capabilities using spatially incoherent illumination. The optical setup of a FINCH-based interferometer is closely related to a radial shearing interferometer, which measures the radial phase difference of an input wavefront. By using phase retrieval methodologies from lateral shearing interferometry, namely, differential Zernike fitting (DZF), we show that FINCH-based and radial shearing interferometry can be used for phase retrieval and adaptive optics (AO). In this paper, we describe the phase retrieval algorithm using least squares-based DZF and demonstrate a simple adaptive optics loop with an aberrated point spread function using wave optics simulation. We find that FINCH-based phase retrieval has the advantages of fast phase retrieval measurements, thanks to well-studied least squares-based phase reconstruction methods, improved resolution compared to the Shack-Hartmann-based wavefront sensing, and the simplified optical setup of radial shearing interferometry.

Recovery of rare earths from rare earth molten salt electrolytic slag using alkali phase reconstruction method with the aid of external electric field

Rare earth molten salt electrolytic slag (RMES) has emerged as a promising secondary resource for rare earth elements (REEs). This study introduces an innovative leaching technique for extracting REEs from RMES under atmospheric conditions, employing an alkali phase reconstruction method followed by an acid leaching process. Additionally, the external electric field was employed to enhance the reaction. Under the optimal reaction conditions: NaOH initial concentration of 70 wt%, NaOH-slag mass ratio of 4:1, temperature of 160 °C, current density of 1000 A/m2, reaction time of 90 min, stirring speed of 300 r/min, HCl concentration of 4 mol/L, liquid-solid ratio of 15:1, and leaching time of 20 min, the leaching efficiencies of Nd and Pr reach up to 99.21% and 99.14%, respectively. Phase analysis indicates that the rare earth fluorides transform into rare earth hydroxides, significantly enhancing their solubility in acid solution. The imposition of an external electric field leads to pronounced disruption of the RMES surface, thereby promoting the formation of stable reactive oxygen species in the alkaline medium. This facilitates the decomposition of fluorinated rare earths and hastens the phase reconstruction, resulting in an enhanced leaching process. The achieved leaching efficiency with an external electric field is 37% higher than that without an electric field.

State of Health Estimation of Lithium-Ion Battery Based on Constant-Voltage Charging Reconstruction

State of health (SOH) estimation is essential for life evaluation and health management of lithium-ion battery (LIB). This paper proposes a novel SOH estimator by using the partial constant-voltage (CV) charging data. First, a thorough analysis is performed over different CV health indicators (HIs) in terms of the HI-SOH correlation as well as the robustness to CV partialness and disturbances, and the CV capacity is proved to be more informative and robust for SOH estimation. Second, to tackle the practical challenge arising from CV charging partialness, a novel CV phase reconstruction method combining Q-V modeling and open-circuit voltage (OCV) estimation iteratively is proposed to predict the CV capacity authentically based on the available partial CV data. The extracted CV capacity is further used to estimate the battery SOH precisely. The proposed method is validated with long-term degradation experiments performed on NCA cells. Results suggest that the proposed method manifests itself with a high estimation accuracy, a low requirement on the charging completeness, and a high robustness to cell inconsistency.

Resolution of Virtual Depth Sectioning from Four-Dimensional Scanning Transmission Electron Microscopy.

One approach to three-dimensional structure determination using the wealth of scattering data in four-dimensional (4D) scanning transmission electron microscopy (STEM) is the parallax method proposed by Ophus et al. (2019. Advanced phase reconstruction methods enabled by 4D scanning transmission electron microscopy, Microsc Microanal25, 10-11), which determines the scattering matrix and uses it to synthesize a virtual depth-sectioning reconstruction of the sample structure. Drawing on an equivalence with a hypothetical confocal imaging mode, we derive contrast transfer and point spread functions for this parallax method applied to weakly scattering objects, showing them identical to earlier depth-sectioning STEM modes when only bright field signal is used, but that improved depth resolution is possible if dark field signal can be used. Through a simulation-based study of doped Si, we show that this depth resolution is preserved for thicker samples, explore the impact of shot noise on the parallax reconstructions, discuss challenges to making use of dark field signal, and identify cases where the interpretation of the parallax reconstruction breaks down.

Sensitivity of heterogeneous microstructure evolution and preferential variant selection to thermomechanical conditions in a near α titanium alloy

Heterogeneous microstructures such as macrozones and Widmannstätten microstructures are intolerable defects in high-temperature aero-engine parts manufactured using titanium (Ti) alloys. Through thermomechanical operations, microstructure characterization, phase reconstruction, and crystal plasticity finite element method (CPFEM) modeling, a full-route analysis was conducted to uncover the microstructure evolution of the allotropic β and α phases and the variant selection of the β→α phase transformation (PT) of a near-α Ti alloy. With an increase in temperature or decrease in strain rate, the continuous dynamic recrystallization (cDRX) of the β phase gradually prevails over the discontinuous dynamic recrystallization (dDRX) of the α phase. The reconstructed high-temperature β microstructure combing the CPFEM simulation revealed that the prior β grains with the deformation-induced 〈100〉//AD orientation, which are softer and grow preferentially via cDRX to form a simple cubic texture in the β phase. The coarse β phase transforms into the secondary α (αs) phase with a localized phase-transformed texture (the 1̅21̅0//AD and 101̅1//AD components) during the subsequent cooling process. Through the reconstructed β phase and crystallographic analysis of the β→α PT, we found that the low-angle grain boundary (LAGB) in the β phase weakens the anisotropic growth of αs grains and the preferential variant selection of 1̅21̅0/60°αs/αs grain boundary (GB) caused by autocatalytic nucleation. A high strain-rate deformation can increase the intragranular defects (e.g., LAGB) of the β phase, which promote the formation of a basket-weave structure instead of a Widmannstätten structure and weakens the variant selection during β→α PT.

An extended Hilbert transform method for reconstructing the phase from an oscillatory signal

Rhythmic activity is ubiquitous in biological systems from the cellular to organism level. Reconstructing the instantaneous phase is the first step in analyzing the essential mechanism leading to a synchronization state from the observed signals. A popular method of phase reconstruction is based on the Hilbert transform, which can only reconstruct the interpretable phase from a limited class of signals, e.g., narrow band signals. To address this issue, we propose an extended Hilbert transform method that accurately reconstructs the phase from various oscillatory signals. The proposed method is developed by analyzing the reconstruction error of the Hilbert transform method with the aid of Bedrosian’s theorem. We validate the proposed method using synthetic data and show its systematically improved performance compared with the conventional Hilbert transform method with respect to accurately reconstructing the phase. Finally, we demonstrate that the proposed method is potentially useful for detecting the phase shift in an observed signal. The proposed method is expected to facilitate the study of synchronization phenomena from experimental data.

Evaluation of multi-channel phase reconstruction methods for quantitative susceptibility mapping on postmortem human brain

Quantitative Susceptibility Mapping (QSM) is an established Magnetic Resonance Imaging (MRI) technique with high potential in brain iron studies associated to several neurodegenerative diseases. Unlike other MRI techniques, QSM relies on phase images to estimate tissue's relative susceptibility, therefore requiring a reliable phase data. Phase images from a multi-channel acquisition should be reconstructed in a proper way. On this work it was compared the performance of combination of phase matching algorithms (MCPC3D-S and VRC) and phase combination methods based on a complex weighted sum of phases, considering the magnitude at different powers (k = 0 to 4) as the weighting factor. These reconstruction methods were applied in two datasets: a simulated brain dataset for a 4-coil array and data of 22 postmortem subjects acquired at a 7T scanner using a 32 channels coil. For the simulated dataset, differences between the ground truth and the Root Mean Squared Error (RMSE) were evaluated. For both simulated and postmortem data, the mean (MS) and standard deviation (SD) of susceptibility values of five deep gray matter regions were calculated. For the postmortem subjects, MS and SD were statistically compared across all subjects. A qualitative analysis indicated no differences between methods, except for the Adaptive approach on postmortem data, which showed intense artifacts. In the 20% noise level case, the simulated data showed increased noise in central regions. Quantitative analysis showed that both MS and SD were not statistically different when comparing k=1 and k=2 on postmortem brain images, however visual inspection showed some boundaries artifacts on k=2. Furthermore, the RMSE decreased (on regions near the coils) and increased (on central regions and on overall QSM) with increasing k. In conclusion, for reconstruction of phase images from multiple coils with no reference available, alternative methods are needed. In this study it was found that overall, the phase combination with k=1 is preferred over other powers of k.

Online Phase Reconstruction via DNN-Based Phase Differences Estimation

This paper presents a two-stage online phase reconstruction framework using causal deep neural networks (DNNs). Phase reconstruction is a task of recovering phase of the short-time Fourier transform (STFT) coefficients only from the corresponding magnitude. However, phase is sensitive to waveform shifts and not easy to estimate from the magnitude even with a DNN. To overcome this problem, we propose to use DNNs for estimating differences of phase between adjacent time-frequency bins. We show that convolutional neural networks are suitable for phase difference estimation, according to the theoretical relation between partial derivatives of STFT phase and magnitude. The estimated phase differences are used for reconstructing phase by solving a weighted least squares problem in a frame-by-frame manner. In contrast to existing DNN-based phase reconstruction methods, the proposed framework is causal and does not require any iterative procedure. The experiments showed that the proposed method outperforms existing online methods and a DNN-based method for phase reconstruction.

Phase Reconstruction Method of High-speed Train Frame Load Based on Correlation Analysis

Phase Reconstruction Method of High-speed Train Frame Load Based on Correlation Analysis

Quantitative phase reconstruction method based on an infrared-like single wave conversion in dual-wavelength phase-shift digital holography

Dual-wavelength digital holography has recently become a promising tool for achieving higher axial measurement ranges in microscopy. However, digital filtering, such as Fourier transform, or a separate hologram acquisition process is essential for retrieving the quantitative phase information of each respective wavelength. In this paper, a quantitative phase reconstruction method based on an infrared-like single wave conversion in dual-wavelength phase-shift digital holography, which does not require any of the processes mentioned above, has been proposed. Based on the proposed method, the quantitative phase information on the axial measurement specimen was simply obtained by only a phase-shift of a quarter of an infrared-like single wave which has the same magnitude of the synthetic wavelength. This approach simplifies not only the hologram acquisition process but also the numerical reconstruction process. The newly proposed theory is verified and evaluated using simulations and experimental validation.

Conceptual design and science cases of a juggled interferometer for gravitational wave detection

The Juggled interferometer (JIFO) is an earth-based gravitational wave detector using repeatedly free-falling test masses. With no worries of seismic noise and suspension thermal noise, the JIFO can have much better sensitivity at lower frequencies than the current earth-based gravitational wave detectors. The data readout method of a JIFO could be challenging if one adopts the fringe-locking method. We present a phase reconstruction method in this paper by building up a complex function which has a fringe-independent signal-to-noise ratio. Considering the displacement noise budget of the Einstein Telescope (ET), we show that the juggled test masses significantly improve the sensitivity at 0.1-2.5$\,$Hz even with discontinuous data. The science cases brought with the improved sensitivity would include detecting quasi-normal modes of black holes with $10^4-10^5\,M_{\odot}$, testing Brans-Dicke theory with black-hole and neutron-star inspirals, and detecting primordial-black-hole-related gravitational waves.

Direct imaging of oxygen shifts associated with the oxygen redox of Li-rich layered oxides

<h2>Summary</h2> Li-rich metal oxides, such as Li_1.2Ni_0.13Mn_0.54Co_0.13O₂, can deliver high specific capacities because of the redox of lattice O²⁻ in addition to the cations. Observing oxygen distortions is key to understand the redox process. Electron ptychography is a phase-reconstruction method in 4D scanning transmission electron microscopy, providing atomic-resolution phase images with high signal-to-noise ratio and dose efficiency. Herein, we use electron ptychography to image the oxygen shift in Li_1.2Ni_0.13Mn_0.54Co_0.13O₂ during the first cycle. The picometer-scale precision measurement shows distinct oxygen shifts in the bulk and surface after charging and compares with various theoretical anionic redox models. The shift after discharging is not seen to recover in the bulk accounting for voltage hysteresis; however, it recovers close to the surface, although with a phase change. We suggest that Li_1.2Ni_0.13Mn_0.54Co_0.13O₂ proceeds distinct oxygen redox in the bulk and surface. The altered oxygen sublattice after first cycle potentially explains the changed voltage profiles of following cycles.

Accurate and fast speckle interferometry in severe illumination conditions

Illumination conditions are an important factor affecting the accuracy of speckle interferometry. High-intensity illumination could cause the speckle images to be overexposed due to the limited dynamic range of the camera, degrading the fringe quality. This study proposes a high-dynamic phase reconstruction method enabling accurate speckle interferometry by means of speckle image fusion. Integrating with phase shifting, a real-time and robust speckle interferometric measurement routine is established by using a heterogeneous concurrency computing. To show the performance, the proposed method is exemplarily applied to shearography with both simulation and experimental studies. Results suggest that the proposed image fusion scheme is effective in dealing with the non-uniform illumination. The proposed linear fusion operator is accordance with the requirement of phase extraction for temporal phase shifting by balancing the image quality and computational efficiency. The innovative method facilitates the use of speckle interferometry in severe conditions where uniform illumination cannot be achieved due to surface conditions.

Zinc-induced phase reconstruction of cobalt–nickel double hydroxide cathodes for high-stability and high-rate nickel–zinc batteries

Nickel–zinc (Ni–Zn) batteries have received extensive attention in the field of energy storage. However, the unsatisfactory durability greatly impedes their wider commercial application. Herein, a novel and efficient zinc-induced phase reconstruction method was reported to boost the electrochemical stability of cobalt–nickel double hydroxide (CoNi-DH) cathode materials for developing high-stability and high-rate Ni–Zn batteries. The inherent mechanism for this phase reconstruction method to achieve the improvement of electrochemical stability was systematically investigated and clarified in this work by combining theoretical calculations and experimental tests. The research results confirmed that the zinc-induced phase reconstruction can produce a new phase of Zn2Co3(OH)10·2H2O in the mixed-phase of CoNi-DH without sacrificing its hierarchical micro-nano structure. This Zn2Co3(OH)10·2H2O phase has a much smaller shrinkage of the lattice spacing of host layers during the redox reaction relative to the pure Co(OH)2 phase, thereby it can inhibit structural damage during the charge–discharge cycle. In addition, the retained hierarchical micro-nano structure can ensure good capacity output. Therefore, the as-fabricated mixed hydroxide electrode and Ni–Zn battery achieve remarkable improvement in the cycling life and exhibit superior rate performance. Moreover, the resultant mixed hydroxide electrode displays good application in the flexible quasi-solid-state Ni-Zn battery.

Robust fringe projection measurement based on reference phase reconstruction

Phase measurement profilometry plays an important role in industrial applications. Traditional fringe projection profilometry (FPP) requires a stable reference plane to record the reference phase for the 3D reconstruction, which makes it sensitive to the motion-induced error caused by the reference plane. Thus, it is challenging for the traditional FPP to implement a robust measurement in industrial scenarios though it exhibits high accuracy. To solve this problem, a phase reconstruction method is proposed where the reference phase is denoised and reconstructed from the background of the modulated phase by minimizing the total variation (TV) via the theory of Sylvester equation. Since this strategy achieves synchronous extraction of the reference phase and the object one, it can correct the reference phase in real-time and reduce the measurement error caused by deviation of the reference plane. The proposed method has been applied to measure the step-shape workpiece with different deviations of the reference plane and the gear workpieces on conveyer. The experimental results show competitive performance with traditional FPP in accuracy and more robustness in complex industrial scenes.

Two-step phase extraction and random phase shift estimation in phase-shifting profilometry based on least-squared optical flow method

Phase shifting profilometry normally requires at least three-frame fringe patterns to extract the phase of objects. In the dynamic measurement field, it is desirable to reduce the number of fringe patterns and eliminate the phase-shifting error caused by the moving of the test object between fringe patterns. In the field of interferometry, the optical flow technique is an outstanding two-step phase reconstruction method. However, the accuracy of the optical flow (OF) algorithm is limited by the residual background intensity and approximation operation as well as noise. In this paper, a phase reconstruction method based on the optical flow algorithm and two-frame least square algorithm (OF&LSA) is proposed to alleviate the phase error caused by the above problems. The proposed method is employed in dynamic phase-shifting profilometry to achieve 2-frame phase reconstruction. The simulation and experiment results verify that the proposed OF&LSA improves the phase accuracy of OF algorithm significantly, and it also performs better than the 3-step phase shifting method in our dynamic measurement experiment.

Interferometric Phase Reconstruction Based on Probability Generative Model: Toward Efficient Analysis of High-Dimensional SAR Stacks

In order to minimize the influence of decorrelation noise on multi-temporal interferometric synthetic aperture radar (MT-InSAR) applications, a series of phase reconstruction methods have been proposed in recent years. Unfortunately, current phase reconstruction methods generally exhibit a low computational efficiency due to their high non-linearity, in particular in the case that the dimension of a SAR stack is high. In this paper, a new approach is proposed to efficiently resolve phase reconstruction problems. This approach is inspired by the theory of probabilistic principle component analysis. A complex valued probability generative model is constructed to portray a phase reconstruction process. Moreover, in order to resolve such a model, a targeted algorithm based on the idea of expectation maximization is designed and implemented. For validation purposes, the proposed approach is compared to the traditional eigenvalue decomposition-based method by using simulated data and 101 real Sentinel-1A SAR images. The experimental results demonstrate that the proposed method can accelerate the phase reconstruction process drastically, in particular when a high-dimensional SAR stack is required to be processed.

Deep learning for label-free nuclei detection from implicit phase information of mesenchymal stem cells.

Monitoring of adherent cells in culture is routinely performed in biological and clinical laboratories, and it is crucial for large-scale manufacturing of cells needed in cell-based clinical trials and therapies. However, the lack of reliable and easily implementable label-free techniques makes this task laborious and prone to human subjectivity. We present a deep-learning-based processing pipeline that locates and characterizes mesenchymal stem cell nuclei from a few bright-field images captured at various levels of defocus under collimated illumination. Our approach builds upon phase-from-defocus methods in the optics literature and is easily applicable without the need for special microscopy hardware, for example, phase contrast objectives, or explicit phase reconstruction methods that rely on potentially bias-inducing priors. Experiments show that this label-free method can produce accurate cell counts as well as nuclei shape statistics without the need for invasive staining or ultraviolet radiation. We also provide detailed information on how the deep-learning pipeline was designed, built and validated, making it straightforward to adapt our methodology to different types of cells. Finally, we discuss the limitations of our technique and potential future avenues for exploration.