Parallel Technologies with Image Processing Using Inverse Filter

Real-time capabilities and computational efficiency are provided by parallel image processing utilizing OpenMP. However, race conditions can affect the accuracy and reliability of the outcomes. This paper highlights the importance of addressing race conditions in parallel image processing, specifically focusing on color inverse filtering using OpenMP. We considered three solutions to solve race conditions, each with distinct characteristics: #pragma omp atomic: Protects individual memory operations for fine-grained control. #pragma omp critical: Protects entire code blocks for exclusive access. #pragma omp parallel sections reduction: Employs a reduction clause for safe aggregation of values across threads. Our findings show that the produced images were unaffected by race condition. However, it becomes evident that solving the race conditions in the code makes it significantly faster, especially when it is executed on multiple cores.

Research on Defect Detection of Wind Turbine Blades Based on Morphology and Improved Otsu Algorithm Using Infrared Images

To address the issues of low accuracy and high false positive rate in traditional Otsu algorithm for defect detection on infrared images of wind turbine blades(WTB),this paper proposes a technique that combines morphological image enhancement with an improved Otsu algorithm.First,mathematical morphology’s differential multi-scale white and black top-hat operations are applied to enhance the image.The algorithm employs entropy as the objective function to guide the iteration process of image enhancement,selecting appropriate structural element scales to execute differential multi-scale white and black top-hat transformations,effectively enhancing the detail features of defect regions and improving the contrast between defects and background.Afterwards,grayscale inversion is performed on the enhanced infrared defect image to better adapt to the improved Otsu algorithm.Finally,by introducing a parameter K to adjust the calculation of inter-class variance in the Otsu method,the weight of the target pixels is increased.Combined with the adaptive iterative threshold algorithm,the threshold selection process is further fine-tuned.Experimental results show that compared to traditional Otsu algorithms and other improvements,the proposed method has significant advantages in terms of defect detection accuracy and reducing false positive rates.The average defect detection rate approaches 1,and the average Hausdorff distance decreases to 0.825,indicating strong robustness and accuracy of the method.

Migration images guided high-resolution velocity modeling based on fully convolutional neural network

Current data-driven deep learning(DL)methods typically reconstruct subsurface velocity models directly from pre-stack seismic records.However,these purely data-driven methods are often less robust and produce results that are less physically interpretative.Here,the authors propose a new method that uses migration images as input,combined with convolutional neural networks to construct high-resolution velocity models.Compared to directly using pre-stack seismic records as input,the nonlinearity between migration images and velocity models is significantly reduced.Additionally,the advantage of using migration images lies in its ability to more comprehensively capture the reflective properties of the subsurface medium,including amplitude and phase information,thereby to provide richer physical information in guiding the reconstruction of the velocity model.This approach not only improves the accuracy and resolution of the reconstructed velocity models,but also enhances the physical interpretability and robustness.Numerical experiments on synthetic data show that the proposed method has superior reconstruction performance and strong generalization capability when dealing with complex geological structures,and shows great potential in providing efficient solutions for the task of reconstructing high-wavenumber components.

Centimeter-sized Cs_(3)Cu_(2)I_(5)single crystals grown by oleic acid assisted inverse temperature crystallization strategy and their films for high-quality X-ray imaging

Low-dimensional halide perovskites have become the most promising candidates for X-ray imaging,yet the issues of the poor chemical stability of hybrid halide perovskite,the high poisonousness of lead halides and the relatively low detectivity of the lead-free halide perovskites which seriously restrain its commercialization.Here,we developed a solution inverse temperature crystal growth(ITCG)method to bring-up high quality Cs_(3)Cu_(2)I_(5)crystals with large size of centimeter order,in which the oleic acid(OA)is introduced as an antioxidative ligand to inhibit the oxidation of cuprous ions effieiently,as well as to decelerate the crystallization rate remarkalby.Based on these fine crystals,the vapor deposition technique is empolyed to prepare high quality Cs_(3)Cu_(2)I_(5)films for efficient X-ray imaging.Smooth surface morphology,high light yields and short decay time endow the Cs_(3)Cu_(2)I_(5)films with strong radioluminescence,high resolution(12 lp/mm),low detection limits(53 nGyair/s)and desirable stability.Subsequently,the Cs_(3)Cu_(2)I_(5)films have been applied to the practical radiography which exhibit superior X-ray imaging performance.Our work provides a paradigm to fabricate nonpoisonous and chemically stable inorganic halide perovskite for X-ray imaging.

New templates of CNN for extracting corners of objects in gray-scale images

A new cellular neural network (CNN) with nonlinear templates is presented forextracting convex corners of objects in gray-scale images. Application examples showed that the newCNN can even detect convex corner characteristics of objects in images with Gaussian noise.

AN ADAPTIVE DIGITAL IMAGE WATERMARK ALGORITHM BASED ON GRAY-SCALE MORPHOLOGY

An adaptive digital image watermark algorithm with strong robustness based on gray-scale morphology is proposed in this paper.The embedded strategies include:The algorithm seeks and extracts adaptively the image strong texture regions.The algorithm maps the image strong texture region to the wavelet tree structures, and embeds adaptively watermark into the wavelet coefficients corresponding to the image's strong texture regions.According to the visual masking features, the algorithm adjusts adaptively the watermark-embedding intensity.Experimental results show the algorithm is robust to compression, filtering, noise as well as strong shear attacks.The algorithm is blind watermark scheme.The image strong texture region extraction method based on morphology in this algorithm is simple and effective and adaptive to various images.

Wavelet inverse scale space for image restoration

This paper proposes a model for image restoration by combining the wavelet shrinkage and inverse scale space （ISS） method. The ISS is applied to the wavelet representation to modify the retained wavelet coefficients, and the coefficients smaller than the threshold are set to zero. The curvature term of the ISS can remove the edge artifacts and preserve sharp edges. For the multiscale interpretation of the ISS and the multiscale property of the wavelet representation, small details are preserved. This paper illustrates that the wavelet ISS model can be deduced from the wavelet based on a total variation minimization problem. A stopping criterion is obtained from this minimization in the sense of the Bregman distance in the wavelet domain. Numerical examples show the improvement for the image denoising with the proposed method in the sense of the signal to noise ratio and with fewer details remained in the residue.

Quantitative ultrasound brain imaging with multiscale deconvolutional waveform inversion

High-resolution images of human brain are critical for monitoring the neurological conditions in a portable and safe manner.Sound speed mapping of brain tissues provides unique information for such a purpose.In addition,it is particularly important for building digital human acoustic models,which form a reference for future ultrasound research.Conventional ultrasound modalities can hardly image the human brain at high spatial resolution inside the skull due to the strong impedance contrast between hard tissue and soft tissue.We carry out numerical experiments to demonstrate that the time-domain waveform inversion technique,originating from the geophysics community,is promising to deliver quantitative images of human brains within the skull at a sub-millimeter level by using ultra-sound signals.The successful implementation of such an approach to brain imaging requires the following items:signals of sub-megahertz frequencies transmitting across the inside of skull,an accurate numerical wave equation solver simulating the wave propagation,and well-designed inversion schemes to reconstruct the physical parameters of targeted model based on the optimization theory.Here we propose an innovative modality of multiscale deconvolutional waveform inversion that improves ultrasound imaging resolution,by evaluating the similarity between synthetic data and observed data through using limited length Wiener filter.We implement the proposed approach to iteratively update the parametric models of the human brain.The quantitative imaging method paves the way for building the accurate acoustic brain model to diagnose associated diseases,in a potentially more portable,more dynamic and safer way than magnetic resonance imaging and x-ray computed tomography.

Micro-motion effect in inverse synthetic aperture radar imaging of ballistic mid-course targets

For ballistic mid-course targets,in addition to constant orbital motion,the target or any structure on the target undergoes micro-motion dynamics,such as spin,precession and tumbling.The micro-motion characteristics of the ballistic mid-course targets were discussed.The target motion model and inverse synthetic aperture radar(ISAR) imaging model for this kind of targets were built.Then,the influence of micro-motion on ISAR imaging based on the established imaging model was presented.The computer simulation to get mid-course target echoes from static darkroom electromagnetic scattering data based on the established target motion model was realized.The imaging results of computer simulation show the validity of ISAR imaging analysis for micro-motion targets.

AN INVERSE DESIGN OF ELECTROSTATIC FOCUSING FIELD FOR ELECTROSTATIC AND MAGNETIC IMAGING

An inverse design of electrostatic focusing field for electrostatic and magneticimaging is investigated.Using the potential superimposition theorem of electrostatic field inmulti-electrode system,a mathematical model has been developed and an optimization methodhas been introduced into computation for designing the electrostatic focusing field of the imagingsystem.

Inverse Gaussian-beam common-reflection-point-stack imaging in crosswell seismic tomography

To solve problems in small-scale and complex structural traps,the inverse Gaussian-beam stack-imaging method is commonly used to process crosswell seismic wave reflection data.Owing to limited coverage,the imaging quality of conventional ray-based crosswell seismic stack imaging is poor in complex areas;moreover,the imaging range is small and with sever interference because of the arc phenomenon in seismic migration.Thus,we propose the inverse Gaussian-beam stack imaging,in which Gaussian weight functions of rays contributing to the geophones energy are calculated and used to decompose the seismic wavefield.This effectively enlarges the coverage of the reflection points and improves the transverse resolution.Compared with the traditional VSP–CDP stack imaging,the proposed methods extends the imaging range,yields higher horizontal resolution,and is more adaptable to complex geological structures.The method is applied to model a complex structure in the K-area.The results suggest that the wave group of the target layer is clearer,the resolution is higher,and the main frequency of the crosswell seismic section is higher than that in surface seismic exploration The effectiveness and robustness of the method are verified by theoretical model and practical data.

Linearized waveform inversion for vertical transversely isotropic elastic media:Methodology and multi-parameter crosstalk analysis

Seismic migration and inversion are closely related techniques to portray subsurface images and identify hydrocarbon reservoirs.Seismic migration aims at obtaining structural images of subsurface geologic discontinuities.More specifically,seismic migration estimates the reflectivity function(stacked average reflectivity or pre-stack angle-dependent reflectivity)from seismic reflection data.On the other hand,seismic inversion quantitatively estimates the intrinsic rock properties of subsurface formulations.Such seismic inversion methods are applicable to detect hydrocarbon reservoirs that may exhibit lateral variations in the inverted parameters.Although there exist many differences,pre-stack seismic migration is similar with the first iteration of the general linearized seismic inversion.Usually,seismic migration and inversion techniques assume an acoustic or isotropic elastic medium.Unconventional reservoirs such as shale and tight sand formation have notable anisotropic property.We present a linearized waveform inversion(LWI)scheme for weakly anisotropic elastic media with vertical transversely isotropic(VTI)symmetry.It is based on two-way anisotropic elastic wave equation and simultaneously inverts for the localized perturbations(ΔVp_(0)/Vp_(0)/Vs_(0)/Vs_(0)/,Δ∈,Δδ)from the long-wavelength reference model.Our proposed VTI-elastic LWI is an iterative method that requires a forward and an adjoint operator acting on vectors in each iteration.We derive the forward Born approximation operator by perturbation theory and adjoint operator via adjoint-state method.The inversion has improved the quality of the images and reduces the multi-parameter crosstalk comparing with the adjoint-based images.We have observed that the multi-parameter crosstalk problem is more prominent in the inversion images for Thomsen anisotropy parameters.Especially,the Thomsen parameter is the most difficult to resolve.We also analyze the multi-parameter crosstalk using scattering radiation patterns.The linearized waveform inversion for VTI-elastic media presented in this article provides quantitative information of the rock properties that has the potential to help identify hydrocarbon reservoirs.

Piecewise Acoustic Source Imaging with Unknown Speed of Sound Using a Level-Set Method

We investigate the following inverse problem:starting from the acoustic wave equation,reconstruct a piecewise constant passive acoustic source from a single boundary temporal measurement without knowing the speed of sound.When the amplitudes of the source are known a priori,we prove a unique determination result of the shape and propose a level set algorithm to reconstruct the singularities.When the singularities of the source are known a priori,we show unique determination of the source amplitudes and propose a least-squares fitting algorithm to recover the source amplitudes.The analysis bridges the low-frequency source inversion problem and the inverse problem of gravimetry.The proposed algorithms are validated and quantitatively evaluated with numerical experiments in 2D and 3D.

A THEORY FOR WAVE EQUATION INVERSE PROBLEM:THE UNION METHOD FOR SCATTERED WAVEEXTRAPOLATION AND VELOCITY IMAGING

A new theory for inverse problem of wave equation, that is, the union method for scattered wave extrapolation and velocity imaging, is proposed in this paper. This method is very different from the classical wave extrapolation for migration, because we relate directly the scattered wave extrapolation to velocity inversion. And also this method is different from any linearized inverse method of wave equation, because we needn′t use linearized approximation. Because of this, the method can be applied to strong scattering case effectively (i.e. the value of scattered wave is not small, which can not be neglected). This method, of course, is different from nonlinearized optimum inverse method, because in this paper, the nonlinear inverse problem is turned into two steps inverse problem, i.e. scattered wave extrapolated and velocity imaging, which can be solved easily. Hence, the problem how to get the global optimum solution by using the nonlinearized optimum inverse method doesn′t bother us by using the method in this paper.

Microwave Through Wall Imaging via An Induced Current Learning Method

In this paper,an induced current learning method(ICLM)for microwave through wall imaging(TWI),named as TWI-ICLM,is proposed.In the inversion of induced current,the unknown object along with the enclosed walls are treated as a combination of scatterers.Firstly,a non-iterative method called distorted-Born backpropagation(DB-BP)is utilized to generate the initial result.In the training stage,several convolutional neural networks(CNNs)are cascaded to improve the estimated induced current.In addition,a hybrid loss function consisting of the induced current error and the permittivity error is used to optimize the network parameters.Finally,the relative permittivity images are conducted analytically using the predicted current based on ICLM.Both the numerical and experimental TWI tests prove that,the proposed method can achieve better imaging accuracy compared to traditional distorted-Born iterative method(DBIM).

Inverse gaussian beam stack imaging in 3D crosswell seismic exploration of deviated wells and Its application

In crosswell seismic exploration,the imaging section produced by migration based on a wave equation has a serious arc phenomenon at its edge and a small effective range because of geometrical restrictions.Another imaging section produced by the VSP-CDP stack imaging method employed with ray-tracing theory is amplitude-preserved.However,imaging 3D complex lithological structures accurately with this method is difficult.Therefore,this study proposes inverse Gaussian beam stack imaging in the 3D crosswell seismic exploration of deviated wells on the basis of Gaussian beam ray-tracing theory.By employing Gaussian beam ray-tracing theory in 3D crosswell seismic exploration,we analyzed the energy relationship between seismic wave fields and their effective rays.In imaging,the single-channel seismic wave fi eld data in the common shot point gather are converted into multiple effective wave fields in the common reflection point gather by the inverse Gaussian beam.The process produces a large fold number of intensive reflection points.Selected from the horizontal and vertical directions of the 2D measuring line,the wave fi elds of the eff ective reflection points in the same stack bin are projected onto the 2D measuring line,chosen according to the distribution characteristics of the reflection points,and stacked into an imaging section.The method is applied to X oilfi eld to identify the internal structure of the off shore gas cloud area.The results provided positive support for the inverse Gaussian beam stack imaging of 3D complex lithological structures and proved that technology is a powerful imaging tool for 3D crosswell seismic data processing.

3D Gravity Inversion with Correlation Image in Space Domain and Application to the Northern Sinai Peninsula

We present a 3D inversion method to recover density distribution from gravity data in space domain.Our method firstly employs 3D correlation image of the vertical gradient of gravity data as a starting model to generate a higher resolution image for inversion.The 3D density distribution is then obtained by inverting the correlation image of gravity data to fit the observed data based on classical inversion method of the steepest descent method.We also perform the effective equivalent storage and subdomain techniques in the starting model calculation,the forward modeling and the inversion procedures,which allow fast computation in space domain with reducing memory consumption but maintaining accuracy.The efficiency and stability of our method is demonstrated on two sets of synthetic data and one set of the Northern Sinai Peninsula gravity data.The inverted 3D density distributions show that high density bodies beneath Risan Aniza and low density bodies exist to the southeast of Risan Aniza at depths between 1~10 and 20 km,which may be originated from hot anomalies in the lower crust.The results show that our inversion method is useful for 3D quantitative interpretation.

Extraction of Desertification Information in Hulun Buir Based on MODIS Image Data

[Objective] To extract desertification information of Hulun Buir region based on MODIS image data. [Method] Based on MODIS image data with the spatial res- olution of 1 km, 5 indicators which could reflect different desertification features were selected to conduct inversion. The desertification information of Hulun Buir region was extracted by decision tree classification. [Result] The desertification area of Hu- lun Buir region is 33 862 km2, accounting for 24% of the total area, and it is mainly dominated by sandiness desertification. Though field verification and mining point validation of high-resolution interpretation data, the overall accuracy of this evaluation is above 89%. [Conclusion] Evaluation method used in this study is not only effectively for large scale regional desertification monitoring but also has a better evaluation performance.

Nonlinear inversion of electrical resistivity imaging using pruning Bayesian neural networks

Conventional artificial neural networks used to solve electrical resistivity imaging （ERI） inversion problem suffer from overfitting and local minima. To solve these problems, we propose to use a pruning Bayesian neural network （PBNN） nonlinear inversion method and a sample design method based on the K-medoids clustering algorithm. In the sample design method, the training samples of the neural network are designed according to the prior information provided by the K-medoids clustering results; thus, the training process of the neural network is well guided. The proposed PBNN, based on Bayesian regularization, is used to select the hidden layer structure by assessing the effect of each hidden neuron to the inversion results. Then, the hyperparameter αk, which is based on the generalized mean, is chosen to guide the pruning process according to the prior distribution of the training samples under the small-sample condition. The proposed algorithm is more efficient than other common adaptive regularization methods in geophysics. The inversion of synthetic data and field data suggests that the proposed method suppresses the noise in the neural network training stage and enhances the generalization. The inversion results with the proposed method are better than those of the BPNN, RBFNN, and RRBFNN inversion methods as well as the conventional least squares inversion.

Three-dimensional forward modeling and inversion of borehole-to-surface electrical imaging with different power sources

Borehole-to-surface electrical imaging （BSEI） uses a line source and a point source to generate a stable electric field in the ground. In order to study the surface potential of anomalies, three-dimensional forward modeling of point and line sources was conducted by using the finite-difference method and the incomplete Cholesky conjugate gradient （ICCG） method. Then, the damping least square method was used in the 3D inversion of the formation resistivity data. Several geological models were considered in the forward modeling and inversion. The forward modeling results suggest that the potentials generated by the two sources have different surface signatures. The inversion data suggest that the low- resistivity anomaly is outlined better than the high-resistivity anomaly. Moreover, when the point source is under the anomaly, the resistivity anomaly boundaries are better outlined than when using a line source.