Coherent Noise Research Articles

Single-shot wavelength-multiplexed phase microscopy under Gabor regime in a regular microscope embodiment

Phase imaging microscopy under Gabor regime has been recently reported as an extremely simple, low cost and compact way to update a standard bright-field microscope with coherent sensing capabilities. By inserting coherent illumination in the microscope embodiment and producing a small defocus distance of the sample at the input plane, the digital sensor records an in-line Gabor hologram of the target sample, which is then numerically post-processed to finally achieve the sample’s quantitative phase information. However, the retrieved phase distribution is affected by the two well-known drawbacks when dealing with Gabor’s regime, that is, coherent noise and twin image disturbances. Here, we present a single-shot technique based on wavelength multiplexing for mitigating these two effects. A multi-illumination laser source (including 3 diode lasers) illuminates the sample and a color digital sensor (conventional RGB color camera) is used to record the wavelength-multiplexed Gabor hologram in a single exposure. The technique is completed by presenting a novel algorithm based on a modified Gerchberg–Saxton kernel to finally retrieve an enhanced quantitative phase image of the sample, enhanced in terms of coherent noise removal and twin image minimization. Experimental validations are performed in a regular Olympus BX-60 upright microscope using a 20X 0.46NA objective lens and considering static (resolution test targets) and dynamic (living spermatozoa) phase samples.

Digital holographic microscopy phase noise reduction based on an over-complete chunked discrete cosine transform sparse dictionary

It is well known that a highly coherent laser is used as the illumination source in the digital holographic microscopy (DHM) recording optical path. Lighting sources and optical components may introduce a lot of coherent noise, which is marked as random noise, into the system. In addition, the sample characteristics and the experimental environment also introduce various random noises. Random noise severely degrades the quality and accuracy of the DHM imaging. To reduce the effect of random noise, this paper proposes a noise reduction method based on an over-complete chunked discrete cosine transform (DCT) sparse dictionary, also called a compressed sensing (CS) sparse representation reconstruction image method. The experimental results show that the reconstructed phase peak's signal-to-noise ratio (PSNR) improves from 53.1230 to 66.4436. The sample feature similarity (FSIM) improved from 0.8171 to 0.9377. The experimental data fully demonstrate that the proposed method can reduce the effects caused by random noise and effectively retain the sample details. Compared to traditional filter methods, it has advantages of noise reduction performance, detail retention performance and processing efficiency. Therefore, the proposed method in this paper will play a more important role in the DHM phase measurement.

Compressive Gate Set Tomography

Flexible characterization techniques that provide a detailed picture of the experimental imperfections under realistic assumptions are crucial to gain actionable advice in the development of quantum computers. Gate set tomography self-consistently extracts a complete tomographic description of the implementation of an entire set of quantum gates, as well as the initial state and measurement, from experimental data. It has become a standard tool for this task but comes with high requirements on the number of sequences and their design, making it already experimentally challenging for only two qubits. In this work, we show that low-rank approximations of gate sets can be obtained from significantly fewer gate sequences and that it is sufficient to draw them at random. This coherent noise characterization however still contains the crucial information for improving the implementation. To this end, we formulate the data processing problem of gate set tomography as a rank-constrained tensor completion problem. We provide an algorithm to solve this problem while respecting the usual positivity and normalization constraints of quantum mechanics. For this purpose, we combine methods from Riemannian optimization and machine learning and develop a saddle-free second-order geometrical optimization method on the complex Stiefel manifold. Besides the reduction in sequences, we numerically demonstrate that the algorithm does not rely on structured gate sets or an elaborate circuit design to robustly perform gate set tomography. Therefore, it is more flexible than traditional approaches. We also demonstrate how coherent errors in shadow estimation protocols can be mitigated using estimates from gate set tomography.5 MoreReceived 25 May 2022Revised 10 January 2023Accepted 25 January 2023DOI:https://doi.org/10.1103/PRXQuantum.4.010325Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum channelsQuantum tomographyQuantum verificationQuantum Information

Deep-learning-based cross-talk free and high-security compressive encryption with spatially incoherent illumination.

Incoherent optical cryptosystem is promising for its immunity against coherent noise and insensitivity to misalignment, and compressive encryption is desirable considering the increasingly demand on the exchange of encrypted data via Internet. In this paper, we propose a novel optical compressive encryption approach with spatially incoherent illumination based on deep learning (DL) and space multiplexing. For encryption, the plaintexts are individually sent to the scattering-imaging-based encryption (SIBE) scheme where they are transformed to scattering images with noise appearances. Afterwards, these images are randomly sampled and then integrated into a single package (i.e., ciphertext) by space multiplexing. The decryption is basically the inverse of the encryption, while it involves an ill-posed problem (i.e., recovering the noise-like scattering image from its randomly sampled version). We demonstrated that such a problem can be well resolved by DL. The proposal is radically free from the cross-talk noise existing in many current multiple-image encryption schemes. Also, it gets rid of the linearity bothering the SIBE and is hence robust against the ciphertext-only attack based on phase retrieval algorithm. We present a series of experimental results to confirm the effectiveness and feasibility of the proposal.

Tutorial: Least Squares Migration and Full Waveform Imaging

A major underlying assumption of migration is that the input data are adequately sampled in terms of surface coverage. In addition, we hope that the subsurface is adequately illuminated, and that the migration algorithm itself is based on an acceptable numerical approximation of the wave equation. However, in general these assumptions and aspirations are never fully met, leading to amplitude imbalance and blurring of the output image. To some extent, this blurring and amplitude imbalance can be removed from the migrated data via application of some form of localised deconvolution, generally referred to as least-squares migration. This image modification can be performed in either the data or the image domain and can be achieved via an iterative or a single pass process, under the assumption that the velocity model is acceptable and that no coherent noise such as multiples, contaminates the input data. In this tutorial, we outline some of the various possible approaches to least-squares migration, and comment on the emerging ideas of adapting full waveform inversion to supplant some of the more expensive forms of least-squares image modification.

Application of Q-FWI-tomography and least-squares migration to improve seismic resolution in Tengiz oil field

Seismic image deterioration is a major problem for structures under complex overburdens. In areas under major faults and salt bodies, it is common to observe amplitude washout zones, poor structural definition, and relatively strong coherent and incoherent seismic noise. We developed a data-driven workflow of Q least-squares migration to mitigate these problems by enhancing the horizontal and vertical resolutions of seismic images. The workflow includes accurate velocity model building with joint full-waveform inversion and tomography to maximize the stacking power of P-wave primaries, Q tomography that balances weak-amplitude washout zones and minimizes swing noises, and least-squares migration with the aid of a constraint map that yields higher-resolution structural images. Its application on a wide-azimuth data set from the Tengiz oil field demonstrates the effective mitigation of fault shadow issues with an overall improvement of image quality. In addition, the uplift in focusing of diffracted energies shows promising improvements in enhancing seismic mega-amplitude events. Thus, the proposed method greatly increases the fidelity of seismic attribute analysis when compared with conventional vintage processing.

Microwave-to-optical transduction with erbium ions coupled to planar photonic and superconducting resonators

Optical quantum networks can connect distant quantum processors to enable secure quantum communication and distributed quantum computing. Superconducting qubits are a leading technology for quantum information processing but cannot couple to long-distance optical networks without an efficient, coherent, and low noise interface between microwave and optical photons. Here, we demonstrate a microwave-to-optical transducer using an ensemble of erbium ions that is simultaneously coupled to a superconducting microwave resonator and a nanophotonic optical resonator. The coherent atomic transitions of the ions mediate the frequency conversion from microwave photons to optical photons and using photon counting we observed device conversion efficiency approaching 10−7. With pulsed operation at a low duty cycle, the device maintained a spin temperature below 100 mK and microwave resonator heating of less than 0.15 quanta.

Wavefield characteristics of common-scatterpoint gathers and their application to seismic processing and interpretation in the Yin’e Basin

The Yin’e Basin, lying in the west of the Inner Mongolia Autonomous Region of China, embodies a complex geologic setting involving diverse and deformed strata of sedimentary and igneous rocks. Two-dimensional seismic data, collected in the western portion of the basin for the frontier exploration of oil and gas, are highly complicated with low signal-to-noise ratio and interference of reflected and diffracted waves. Seismic imaging using conventional technologies was poor, and the subsurface structures and strata were unreliable for interpretation. We have applied an integrated processing-and-interpretation method based on seismic wavefield characteristics of common-scatterpoint (CSP) gathers to deal with the exploration challenges. First, we enhanced the amplitude of signals and distinguished weak signals from coherent noises by their kinematic characteristics. The signals, including reflected and diffracted waves, appear as scattered waves with highlighted hyperbolic curves, characterized by the vertex of the hyperbola coinciding with the subsurface scatterpoint and the curvature determined by the root-mean-square velocity of the overlying medium. In contrast, the coherent noises, such as surface and refracted waves, present nearly linear curves with remarkably intensified amplitude in CSP gathers. Then, we established an accurate velocity model and investigated the thickness of the strata. Finally, we ran a prestack time migration (PSTM) for reflected and diffracted waves. The images were improved by mapping signals to the correct position with accurate velocity; therefore, the target strata and the fault planes, with the higher lateral resolution, were more visible. With the aid of well control and the velocity model, main faults and target sequences over the surveyed area were interpreted. Our CSP-gather processing technique not only has enabled us to reveal subsurface complexities and reach the exploration target in the area but also has implications for seismic explorations in similar environments where structures and sequences vary dramatically.

HoMeNL: A Homogeneity Measure-Based NonLocal Filtering Framework for Detail-Enhanced (Pol)(In)SAR Image Denoising

As an inherent problem in coherent imaging systems, the existence of speckle noise results in SAR images with strong signal-dependent variance and seriously hinders the related properties estimation and the image interpretation. Among many filtering methods, nonlocal means (NLM) have been proven to be effective in reducing noise while preserving details. However, traditional NLM filters still face two core problems: 1) it is difficult for homogeneous pixels selection to construct a patch adaptive to local structure for preventing the omission phenomenon; 2) most central pixel value estimators are still at the stage of suppressing the blurring effect rather than eliminating the wrongly selected heterogeneous pixels. To overcome these two problems in the (Pol)(In)SAR image denoising, this paper proposes a homogeneity measure-based nonlocal (HoMeNL) filtering framework based on the following three innovations: 1) to sufficiently select homogeneous pixels in the patch-wise matching processing, the shape-adaptive (SA) patch can be selected from multiple preset patches with the one-to-many matching strategy; 2) as a general extension of the Lee estimator in (Pol)(In)SAR image denoising, the homogeneity measure (HoMe)-based estimator can achieve an optimal bias-variance tradeoff for the central pixel value; 3) the highlight of the proposed method is that the iterative re-weighted (IRW) estimation combines the residuals statistics and the homogeneity measure to adaptively locate and remove the wrongly selected heterogeneous pixels. Simulated and real experimental results show that the proposed filtering framework owns a superior performance than most state-of-art filters in three aspects of noise reduction, detail enhancement, and coherence magnitude estimation.

Joint DOA, range and polarization estimation based on geometric algebra for near-field non-circular source with a symmetric MIMO array

Multiple-input multiple-output (MIMO) radar is a new type of radar with excellent performance in target detection and parameter estimations. The MIMO radar equipped with electromagnetic vector sensors (EMVSs) can record multi-dimensional information of the electromagnetic (EM) signal. Traditional methods based on long vector model cannot preserve orthogonality between the outputs of each component of the EMVS and have high computational complexity. In this paper, a novel geometric algebra (GA)-based localization method is proposed for direction of arrival (DOA), range, and polarization estimation of near-field (NF) non-circular (NC) sources for a symmetric MIMO array. First, the proposed method converts the output of an EMVS into a multi-vector through a GA matrix transformation. Then, the DOA and range parameters can be estimated by searching the one-dimensional (1-D) spectrum based on the GA-based NC rank reduction (GANC-RARE) algorithm. Finally, the polarization can be estimated by employing estimated DOAs and ranges based on the modified GA-based NC multiple signal classification (MGANC-MUSIC) algorithm. The computational complexity analysis demonstrates that our solution improves memory spaces of the covariance matrix and reduces computation efforts of the eigenvalue decomposition (EVD). The simulation results show the superiority and reliability of our proposed method in model error and coherent noise environment.

Attenuating coherent environmental noise in seismic data via the U-net method

Noise attenuation is a key step in seismic data processing to enhance desired signal features, minimize artifacts, and avoid misinterpretation. However, traditional attenuation methods are often time-consuming and require expert parameter selection. Deep learning can successfully suppress various types of noise via a trained neural network, potentially saving time and effort while avoiding mistakes. In this study, we tested a U-net method to assess its usefulness in attenuating repetitive coherent events (e.g., pumpjack noise) and to investigate the influence of gain methods on denoising quality. We used the U-net method because it preserves fine-scale information during training. Its performance is controlled by network parameters and improved by minimizing misfits between true data and network estimates. A gain method is necessary to avoid the network’s parameter optimization being biased toward large values in data. We first generated synthetic seismic data with added noise for training. Next, we recovered amplitudes using an automatic gain control (AGC) or a 2D AGC (using adjacent traces’ amplitudes). Then, a back-propagation algorithm minimized the Euclidean norm cost function to optimize the network parameters for better performance. The updating step size and direction were determined using an adaptive momentum optimization method. Finally, we removed the gain effect and evaluated the denoising quality using a normalized root-mean-square error (RMSE). Based on RMSE, the data pre-processed by the 2D AGC performed better with RMSE decreasing from 0.225 to 0.09. We also assessed the limitations of the network when source wavelets or noise differed from the training set. The denoising quality of the trained network was sensitive to the change in the wavelet and noise type. The noisy data in the limitation test set were not substantially improved. The trained network was also tested on the seismic field data collected at Hussar, Alberta, by the CREWES Project. The data had not only excellent reflection events but also substantial pumpjack noise on some shot gathers. We were able to significantly reduce the noise (favorably in comparison to traditional techniques) to considerably allow greater reflection continuity. U-net noise reduction techniques show considerable promise in seismic data processing.

Ultrasonic defect characterization using Bayesian inversion and scattering matrix denoising neural networks

The use of scattering matrix and deep neural networks for ultrasonic characterization of inclined crack-like defects in noisy environments was explored. A distortion model was utilized to simulate coherent noises that could contaminate the experimental measurements in practice. Given a test scattering matrix, we first developed an approach for estimating the parameters of the distortion model based on the structural similarity index. Subsequently, a Bayesian inversion approach was adopted to determine the proportion of positive-angled cracks that should be included in the scattering matrix database. Based on this result, a deep neural network model was constructed and used in the denoising procedure, which can effectively reduce the characterization error induced by measurement noise. The simulation showed that the proposed approach can be reliably used for characterization of crack-like defects with large orientation angles relative to the array direction (e.g., 75∘) and small sizes (e.g., 0.8λ). In experiments, six crack-like defects with orientation angles of 60∘ and 75∘ were characterized with errors within 0.1λ (i.e., 0.25 mm) and 5∘ in size and angle, respectively. In addition, the characterization uncertainty measured by the root-mean-squared error was reduced by 44.7% in size compared with the conventional Bayesian approach.

Smooth magnetotelluric impedance estimation by optimization

In urban and industrialised areas, magnetotelluric (MT) recordings often contain a significant amount of high-power near-field noise which, constituting non-Gaussian noise for most of the recording length, may lead to the failure of the common transfer functions (TF) estimation methods. The most evident consequence of this is the appearance of sharp points in the TF curves versus frequency, deteriorating their inherent smoothness. The TF's smoothness is in fact one of the main criteria adopted by MT practitioners to evaluate the data quality and effectiveness of the estimation procedure; when not reached by the estimation, it may be a-posteriori introduced by splines or filtering procedures, but these approaches lack physical soundness. We propose a new heuristic algorithm aimed at obtaining the maximum TF versus frequency smoothness, through an optimization scheme rejecting EM events in frequency-domain. The algorithm searches for frequency-dependent power thresholds to be applied to the events, rejecting those making the TF sharp. The TF's smoothness is the objective function to be minimized, and the model space is constituted by the infinite set of threshold vectors for each analysed frequency. After the process, the Ordinaly Least Squares (OLS) residual distribution is much closer to a Rayleigh one, allowing for an unbiased estimation. We applied the procedure both to synthetic and real MT data. Measured data has been collected for a geothermal exploration project in the East Africa Rift, where strong coherent noise affected the recordings. The combination of the technique with the remote-reference technique effectively reduced the bias due to the artificial signal. The physical consistency of the resulting TF has been tested by dispersion relationships.

Spatial and temporal variation of three-dimensional ship noise coherence in a submarine canyon.

Ship noise recorded by a vertical hydrophone array in the Mississippi Canyon region of the Gulf of Mexico is shown to contain the acoustic influence of bathymetric features, seabed properties, and water column sound speeds. Noise coherence is demonstrated to be an effective metric not just for identifying ship traffic in recorded data but also for "fingerprinting" the environment. A three-dimensional acoustics model adopting automatic identification system ship track information and realistic oceanographic conditions is used to compute noise coherence distributions across the canyon area and enables numerical study of the water column sound speed effects that can lead to temporal changes in noise coherence. The study shows the importance of including in situ sound speed measurements or constraints in passive ship noise localization from coherence measurements. Seasonal variability is also examined with models suggesting a strong influence of seasonal changes.

Phase noise suppression based on iterative adaptive bilateral filter in digital holographic microscopy for topography measurement

Topography measurement is a pivotal application in quantitative phase imaging using digital holographic microscopy. In this paper, a reflection-type digital holographic microscope is set up to reconstruct the phase image of the sample with step characteristics, and a noise suppression approach based on an iterative adaptive bilateral filter is proposed to reduce phase coherent noise. The proposed denoising approach considers the phase difference in the neighbourhood to better identify and preserve step characteristics. Experimental results demonstrated the effectiveness of the proposed method.

Designing Filter Functions of Frequency-Modulated Pulses for High-Fidelity Two-Qubit Gates in Ion Chains

High-fidelity two-qubit gates in quantum computers are often hampered by fluctuating experimental parameters. The effects of time-varying parameter fluctuations lead to coherent noise on the qubits, which can be suppressed by designing control signals with appropriate filter functions. Here, we develop filter functions for M{\o}lmer-S{\o}rensen gates of trapped-ion quantum computers that accurately predict the change in gate error due to small parameter fluctuations at any frequency. We then design the filter functions of frequency-modulated laser pulses, and compare this method with pulses that are robust to static offsets of the motional-mode frequencies. Experimentally, we measure the noise spectrum of the motional modes and use it for designing the filter functions, which improves the gate fidelity from 99.23(7)% to 99.55(7)% in a five-ion chain.

Pre-stack Fidelity Denoising Technique in Loess Plateau Area

Based on the data of P work area in Ordos Basin, this paper makes a comparative analysis and summarizes its difficulties: there are many kinds of interference sources in the work area, linear noise is developed, external interference, abnormal noise interference and other background noise are serious, and the signal-to-noise ratio of the original data is low due to the influence of the thickness of the low deceleration zone, the thickness of loess cover and the excitation and reception conditions. Therefore, aiming at this difficulty, a set of pre stack fidelity denoising technology is adopted according to different noise types. For surface wave, refraction wave, linear interference, abnormal energy interference and other noise, classification, zoning, step-by-step, domain, frequency and time-sharing fidelity processing are used to improve the data imaging effect. Frequency division abnormal amplitude suppression technology, prestack coherent noise suppression and ground roll wave simulated removal technology are used to solve the problem of noise interference. The results show that the signal-to-noise ratio and resolution of seismic data in this area have been significantly improved by using the above technologies.

Resolution Enhancement Method of L(0,2) Ultrasonic Guided Wave Signal Based on Variational Mode Decomposition, Wavelet Transform and Improved Split Spectrum Processing

Pipeline systems are prone to defects due to the harsh service conditions, which may induce catastrophic failure if found not in time. Ultrasonic guided wave (UGW) testing provides a convenient option for pipeline detection, showing high-efficiency, non-contact, long-distance and large-scale capabilities. To address the problem that UGW signals suffer from poor signal resolution that is mainly related to the coherent noise caused by the dispersion, multi-mode and mode conversion, an advanced signal processing method called VWISSP, based on variational mode decomposition (VMD), wavelet transform (WT), and improved split spectrum processing (ISSP) was proposed, of which SSP was improved by replacing the Gaussian filter bank with cosine filters of constant frequency-to-bandwidth and frequency-to-filter spacing ratios. Compared with ISSP, VWISSP shows better higher accuracy and resolution processing effects to noisy multi-defect UGW signals, which is manifested through the improvement of both the signal-to-noise ratio gain and the defect-to-noise gain. Only feature signals (defects and pipe end) are retained, whereas noise signals are eliminated completely.

Coherent Noise Attenuation by Kurtosis-Guided Adaptive Dictionary Learning Based on Variational Sparse Representation

In seismic data denoising, coherent noise is usually challenging to remove because it has similar features with signal. Therefore, attenuating coherent noise is of great importance for seismic data. In order to obtain a good denoising result, it is necessary to fully consider the features of coherent noise and choose the appropriate denoising methods. Here, we propose a kurtosis-guided adaptive dictionary learning (KGADL) algorithm based on variational sparse representation model. First, the variational sparse representation is to construct initial dictionary that depends on the seismic data, so that it can accurately contain the features of the seismic data, so as to improve the accuracy of sparse representation. Then, we use the K-singular value decomposition (K-SVD) algorithm to update the dictionary and introduce kurtosis to measure each atom after updating. Atoms with high kurtosis values usually exhibit strong irregularities and are considered noise, while atoms with low kurtosis values have more feature of valid waves and are considered signal. The atoms with low kurtosis values characterizing valid signal are retained to get the special dictionary that adapts to the complexity of the input seismic data, and this dictionary is used to suppress the coherent noise in seismic data. Finally, the denoising performance of the proposed method is illustrated by examples of both synthetic and field seismic data.

Seismic Data Enhancement Based on Common-Reflection-Surface-Based Local Slope and Trimmed Mean Filter

Seismic data often contain noise that can disturb or mask effective information. Noise elimination is an important and challenging task in seismic signal processing. Considering the high amplitude continuity of seismic events in the shot domain, this article proposes a structure-oriented denoising method that can enhance the effective events and suppress disturbing noise, including both incoherent and coherent noise. Based on the common-reflection-surface (CRS) travel time, the local slope of seismic events in the shot domain is deduced and estimated to provide structural information for plane-wave prediction. The proposed CRS-based slope depends on fewer parameters (two in 2D) than the conventional full CRS travel time (three in 2D), making it computationally efficient. Using the local slope, the third dimension is created using the plane-wave differential equation to predict the current trace from its neighbor traces and trimmed mean filtering (TMF) is applied in this dimension. The added dimension can be regarded as flattening the seismic events within a neighboring window and collapsing after application of the TMF. Synthetic and field datasets are employed to demonstrate the effectiveness of the proposed structure-oriented TMF. Compared with wavelet and plane-wave destruction methods, the proposed method can preserve more useful information with greater continuity in amplitude.