Internal multiples are commonly present in seismic data due to variations in velocity or density of subsurface media.They can reduce the signal-to-noise ratio of seismic data and degrade the quality of the image.With ...Internal multiples are commonly present in seismic data due to variations in velocity or density of subsurface media.They can reduce the signal-to-noise ratio of seismic data and degrade the quality of the image.With the development of seismic exploration into deep and ultradeep events,especially those from complex targets in the western region of China,the internal multiple eliminations become increasingly challenging.Currently,three-dimensional(3D)seismic data are primarily used for oil and gas target recognition and drilling.Effectively eliminating internal multiples in 3D seismic data of complex structures and mitigating their adverse effects is crucial for enhancing the success rate of drilling.In this study,we propose an internal multiple prediction algorithm for 3D seismic data in complex structures using the Marchenko autofocusing theory.This method can predict the accurate internal multiples of time difference without an accurate velocity model and the implementation process mainly consists of several steps.Firstly,simulating direct waves with a 3D macroscopic velocity model.Secondly,using direct waves and 3D full seismic acquisition records to obtain the upgoing and down-going Green's functions between the virtual source point and surface.Thirdly,constructing internal multiples of the relevant layers by upgoing and downgoing Green's functions.Finally,utilizing the adaptive matching subtraction method to remove predicted internal multiples from the original data to obtain seismic records without multiples.Compared with the two-dimensional(2D)Marchenko algo-rithm,the performance of the 3D Marchenko algorithm for internal multiple prediction has been significantly enhanced,resulting in higher computational accuracy.Numerical simulation test results indicate that our proposed method can effectively eliminate internal multiples in 3D seismic data,thereby exhibiting important theoretical and industrial application value.展开更多
As same as the conventional inverse synthetic aperture radar（ISAR）, the compressed ISAR also requires the echo signal based motion compensation, which consists of the range alignment and the phase autofoeusing. A ph...As same as the conventional inverse synthetic aperture radar（ISAR）, the compressed ISAR also requires the echo signal based motion compensation, which consists of the range alignment and the phase autofoeusing. A phase autofocusing algorithm for compressed ISAR imaging is presented. In the algorithm, phase autofocusing for the sparse ISAR echoes is accomplished using the eigenvector method. Experimental results validate the effectiveness of the algorithm.展开更多
Most operating radar systems don′t have sufficient frequency bandwidth to produce high range resolution(HRR) profile of a target. But we can use stepped frequency waveform in a narrow band coherent radar to obtai...Most operating radar systems don′t have sufficient frequency bandwidth to produce high range resolution(HRR) profile of a target. But we can use stepped frequency waveform in a narrow band coherent radar to obtain the HRR profile of a target. For moving targets which are of great importance in practical radar usage, autofocusing,i.e. phase correction, is a necessary and critical step of the synthetic HRR processing. The purpose of autofocusing is to remove the radial motion effect of the target from radar echoes, and only reserve the stepped frequency effect which is the basis of synthetic HRR capability. We investigate two autofocusing approaches for synthetic HRR radars using stepped frequency waveform in this paper. The first is motion fitting method. This method depends on a certain parametric model, and is computationally expensive. Then we propose the iterative dominant scatterer method. It is robust, non parametric and simple in computation in comparison with the motion fitting method. Experimental results based on data acquired by using a metallised scale model B 52 in a microwave anechoic chamber reveal the validity and effectiveness of the method.展开更多
This paper presents the construction of two kinds of focusing measure operators defined in wavelet domain. One mechanism is that the Discrete Wavelet Transform （DWT） coefficients in high frequency subbands of in-foc...This paper presents the construction of two kinds of focusing measure operators defined in wavelet domain. One mechanism is that the Discrete Wavelet Transform （DWT） coefficients in high frequency subbands of in-focused image are higher than those of defocused one. The other mechanism is that the autocorrelation of an in-focused image filtered through Continuous Wavelet Transform （CWT） gives a sharper profile than blurred one does. Wavelet base, scaling factor and form to get the sum of high frequency energy are the key factors in constructing the operator. Two new focus measure operators are defined through the autofocusing experiments on the micro-vision system of the workcell for micro-aligmnent. The performances of two operators can be quantificationally evaluated through the comparison with two spatial domain operators--Brenner Function （BF） and Squared Gradient Function （SGF）. The focus resolution of the optimized DWT-based operators is 14% higher than that of BF and its computational cost is 52% approximately lower than BF＇s. The focus resolution of the optimized CWT-based operators is 41% lower than that of SGF whereas its computational cost is approximately 36% lower than SGF＇s. It shows that the wavelet based autofocus measure functions can be practically used in micro-vision applications.展开更多
We numerically and experimentally demonstrate that a three-Airy autofocusing beam can be generated by superposing three deformed two-dimensional(2D)Airy beams with a triangle symmetry.When the initial angle between tw...We numerically and experimentally demonstrate that a three-Airy autofocusing beam can be generated by superposing three deformed two-dimensional(2D)Airy beams with a triangle symmetry.When the initial angle between two wings of the deformed 2D Airy beams increases,such a three-Airy autofocusing beam exhibits that the focusing length decreases and the intensity contrast at the focal point changes.Moreover,after introducing an optical vortex phase,this three-Airy autofocusing beam displays a transverse rotation in propagation.The rotation angle is determined by the topological charge of the vortex and the initial wing angle.Our results may have some potential applications in optical manipulation.展开更多
A simple model of the phase-detection autofocus device based on the partially masked sensor pixels is described. The cross-correlation function of the half-images registered by the masked pixels is proposed as a focus...A simple model of the phase-detection autofocus device based on the partially masked sensor pixels is described. The cross-correlation function of the half-images registered by the masked pixels is proposed as a focus function. It is shown that—in such setting—focusing is equivalent to searching of the cross-correlation function maximum. Application of stochastic approximation algorithms to unimodal and non-unimodal focus functions is shortly discussed.展开更多
The phase error estimated by phase gradient autofocus(PGA) is not based on a finite order polynomial mode, so PGA has a good autofocus property for arbitrary order phase error and is fit for high resolution airborne S...The phase error estimated by phase gradient autofocus(PGA) is not based on a finite order polynomial mode, so PGA has a good autofocus property for arbitrary order phase error and is fit for high resolution airborne SAR. But PGA has two shortcomings: first, it has a worse estimation property for fast changing phase error; second, there exists a section of linear phase in the phase error estimated by this algorithm. This paper introduces the idea of rank one phase estimate (ROPE) autofocus technique, and improves PGA. The improved PGA(IPGA) can successfully overcome both these shortcomings of PGA.展开更多
A new approach to phase averaging in phase gradient algorithm (PGA) is proposed, which is based on the fundamental fact that the information of translational phase error is widely contained in every defocused scattere...A new approach to phase averaging in phase gradient algorithm (PGA) is proposed, which is based on the fundamental fact that the information of translational phase error is widely contained in every defocused scatterer in ISAR image. The new approach aims to choose strong scatterers for error phase averaging with a threshold rather than just simply to pick out the strongest point in each range cell, which is not necessarily real strong scatterers if in some range cells consists more than one strong scatterer and whereas in other range cells no scatterer at all. The results of processing real data are presented to confirm the validity of the proposed approach.展开更多
Autofocus method based on the analysis of image content information is investigated to reduce the alignment error resulting from mark positioning uncertainty due to defocus in microstructure layered fabrication proces...Autofocus method based on the analysis of image content information is investigated to reduce the alignment error resulting from mark positioning uncertainty due to defocus in microstructure layered fabrication process based on multilevel imprint lithography. The applicability of several autofocus functions to the alignment mark images is evaluated concerning their uniformity, sharpness near peak, reliability and measure computation efficiency and the most suitable one based on power spectrum in frequency domain (PSFD) is adopted. To solve the problem of too much computation amount needed in PSFD algorithm, the strategy of interested region detection and effective image reconstruction is proposed and the algorithm efficiency is improved. The test results show that the computation time is reduced from 0.316 s to 0.023 s under the same conditions while the other merits of the function are preserved, which indicates that the modified algorithm can meet the mark image autofocusing requirements in response time, accuracy and robustness. The alignment error due to defocus which is about 0.5 μm indicated by experimental results can be reduced or eliminated by the autofocusing implementation.展开更多
Turbulence in complex environments such as the atmosphere and biological media has always been a great challenge to the application of beam propagation in optical communication, optical trapping and manipulation. To o...Turbulence in complex environments such as the atmosphere and biological media has always been a great challenge to the application of beam propagation in optical communication, optical trapping and manipulation. To overcome this challenge, this study comprehensively investigates the robust propagation of traditional Gaussian and autofocusing beams in turbulent environments. In order to select stable beams that exhibit high intensity and high field gradient at the focal position in complex environments, Kolmogorov turbulence theory is used to simulate the propagation of beams in atmospheric turbulence based on the multi-phase screen method. We systematically analyze the intensity fluctuations, the variation of the coherence factor and the change in the scintillation index with propagation distance. The analysis reveals that the intensity fluctuations of autofocusing beams are significantly smaller than those of Gaussian beams, and the coherence of autofocusing beams is better than that of Gaussian beams under turbulence. Moreover, autofocusing beams exhibit less oscillation than Gaussian beams, indicating that autofocusing beams propagate in complex environments with less distortion and intensity fluctuation. Overall, this work clearly demonstrates that autofocusing beams exhibit higher stability in propagation compared with Gaussian beams, showing great promise for applications such as optical trapping and manipulation in complex environments.展开更多
This paper proposes an autofocus algorithm used for Synthetic Aperture Radar (SAR) images, called Adaptive Kurtosis Optimization Autofocus Algorithm (AKOAA). The AKOAA can reduce the differ-ence between initial value ...This paper proposes an autofocus algorithm used for Synthetic Aperture Radar (SAR) images, called Adaptive Kurtosis Optimization Autofocus Algorithm (AKOAA). The AKOAA can reduce the differ-ence between initial value and real value in focusing by adaptively adjusting the initial value, therefore effec-tively improve the local extremum problem in the Contrast Optimization Autofocus Algorithm (COAA) and speed up the convergence velocity. The principle and realization method of AKOAA are thoroughly investi-gated, and experimental results using real L-band SAR data show that the focus speed of AKOAA is nearly doubled compared with that of the COAA, and the image contrast of AKOAA is improved as well.展开更多
In this paper,we present a high speed autofocus system for micro system applications and design a look-up-table based autofocusing algorithm for applications when a target object is always visible,e.g.,manufacturing p...In this paper,we present a high speed autofocus system for micro system applications and design a look-up-table based autofocusing algorithm for applications when a target object is always visible,e.g.,manufacturing parts with alignment fiducials.We perform an evaluation of 24 focus measures to verify that which focus measure is the best for the look-up-table based method.From the evaluation,we find that the Chebyshev moments-based focus measure（CHEB） is the most suitable.Furthermore,we also develop a look-up-table based autofocus system that uses CHEB as the focus measure.In training phase,we offline construct a table from training images of an object that are captured at several lens distances.Each entry of table consists of focus measure computed from image and lens distance.In working phase,given an input image,the algorithm first computes the focus measure and then finds the best match focus measure from the table and looks up the corresponding lens position for moving it into the in-focus position.Our algorithm can perform autofocusing within only 2 steps of lens moving.The experiment shows that the system can perform high speed autofocusing of micro objects.展开更多
随着采用雷达天文学技术开展行星成像研究的发展,具备高灵敏度的500 m口径球面射电望远镜(Five Hundred Meter Aperture Spherical Radio Telescope,简称FAST)也开展了月球雷达成像的相关研究。基于由三亚非相干散射雷达(Sanya Incohere...随着采用雷达天文学技术开展行星成像研究的发展,具备高灵敏度的500 m口径球面射电望远镜(Five Hundred Meter Aperture Spherical Radio Telescope,简称FAST)也开展了月球雷达成像的相关研究。基于由三亚非相干散射雷达(Sanya Incoherent Scatter Radar,SYISR)和FAST组成的双地基雷达系统,采用包络对齐和相位校正方法,修正月地间相对运动带来的雷达回波间的误差。由于FAST具有高灵敏度,并且雷达回波的信噪比高,因此包络对齐仅采用曲线拟合技术就基本可以满足需求。在相位校正方面,为了提高精度并降低时间消耗,提出采用相位梯度自聚焦算法与最小熵自聚焦算法相结合的方法。实验证明,此方法可有效提高月球图像对比度,降低图片熵值。展开更多
文摘Internal multiples are commonly present in seismic data due to variations in velocity or density of subsurface media.They can reduce the signal-to-noise ratio of seismic data and degrade the quality of the image.With the development of seismic exploration into deep and ultradeep events,especially those from complex targets in the western region of China,the internal multiple eliminations become increasingly challenging.Currently,three-dimensional(3D)seismic data are primarily used for oil and gas target recognition and drilling.Effectively eliminating internal multiples in 3D seismic data of complex structures and mitigating their adverse effects is crucial for enhancing the success rate of drilling.In this study,we propose an internal multiple prediction algorithm for 3D seismic data in complex structures using the Marchenko autofocusing theory.This method can predict the accurate internal multiples of time difference without an accurate velocity model and the implementation process mainly consists of several steps.Firstly,simulating direct waves with a 3D macroscopic velocity model.Secondly,using direct waves and 3D full seismic acquisition records to obtain the upgoing and down-going Green's functions between the virtual source point and surface.Thirdly,constructing internal multiples of the relevant layers by upgoing and downgoing Green's functions.Finally,utilizing the adaptive matching subtraction method to remove predicted internal multiples from the original data to obtain seismic records without multiples.Compared with the two-dimensional(2D)Marchenko algo-rithm,the performance of the 3D Marchenko algorithm for internal multiple prediction has been significantly enhanced,resulting in higher computational accuracy.Numerical simulation test results indicate that our proposed method can effectively eliminate internal multiples in 3D seismic data,thereby exhibiting important theoretical and industrial application value.
基金Supported by the National Natural Science Foundation of China(61071165)the Program for NewCentury Excellent Talents in University(NCET-09-0069)the Defense Industrial Technology Development Program(B2520110008)~~
文摘As same as the conventional inverse synthetic aperture radar（ISAR）, the compressed ISAR also requires the echo signal based motion compensation, which consists of the range alignment and the phase autofoeusing. A phase autofocusing algorithm for compressed ISAR imaging is presented. In the algorithm, phase autofocusing for the sparse ISAR echoes is accomplished using the eigenvector method. Experimental results validate the effectiveness of the algorithm.
文摘Most operating radar systems don′t have sufficient frequency bandwidth to produce high range resolution(HRR) profile of a target. But we can use stepped frequency waveform in a narrow band coherent radar to obtain the HRR profile of a target. For moving targets which are of great importance in practical radar usage, autofocusing,i.e. phase correction, is a necessary and critical step of the synthetic HRR processing. The purpose of autofocusing is to remove the radial motion effect of the target from radar echoes, and only reserve the stepped frequency effect which is the basis of synthetic HRR capability. We investigate two autofocusing approaches for synthetic HRR radars using stepped frequency waveform in this paper. The first is motion fitting method. This method depends on a certain parametric model, and is computationally expensive. Then we propose the iterative dominant scatterer method. It is robust, non parametric and simple in computation in comparison with the motion fitting method. Experimental results based on data acquired by using a metallised scale model B 52 in a microwave anechoic chamber reveal the validity and effectiveness of the method.
基金Hi-Tech Research and Development Program of China(2002AA404460,2004AA404260)National "15","211 Project" ofChina
文摘This paper presents the construction of two kinds of focusing measure operators defined in wavelet domain. One mechanism is that the Discrete Wavelet Transform （DWT） coefficients in high frequency subbands of in-focused image are higher than those of defocused one. The other mechanism is that the autocorrelation of an in-focused image filtered through Continuous Wavelet Transform （CWT） gives a sharper profile than blurred one does. Wavelet base, scaling factor and form to get the sum of high frequency energy are the key factors in constructing the operator. Two new focus measure operators are defined through the autofocusing experiments on the micro-vision system of the workcell for micro-aligmnent. The performances of two operators can be quantificationally evaluated through the comparison with two spatial domain operators--Brenner Function （BF） and Squared Gradient Function （SGF）. The focus resolution of the optimized DWT-based operators is 14% higher than that of BF and its computational cost is 52% approximately lower than BF＇s. The focus resolution of the optimized CWT-based operators is 41% lower than that of SGF whereas its computational cost is approximately 36% lower than SGF＇s. It shows that the wavelet based autofocus measure functions can be practically used in micro-vision applications.
基金National Natural Science Foundation of China(Grant No.11604058)Natural Science Foundation of Ningbo City,China(Grant No.ZX2015000617)+1 种基金the K C Wong Magna Fund in Ningbo University,Chinathe Natural Science Foundation of Guangxi Zhuang Autonomous Region,China(Grant Nos.2016GXNSFBA380244 and 2015GXNSFBA139011).
文摘We numerically and experimentally demonstrate that a three-Airy autofocusing beam can be generated by superposing three deformed two-dimensional(2D)Airy beams with a triangle symmetry.When the initial angle between two wings of the deformed 2D Airy beams increases,such a three-Airy autofocusing beam exhibits that the focusing length decreases and the intensity contrast at the focal point changes.Moreover,after introducing an optical vortex phase,this three-Airy autofocusing beam displays a transverse rotation in propagation.The rotation angle is determined by the topological charge of the vortex and the initial wing angle.Our results may have some potential applications in optical manipulation.
基金supported by the NCN grant UMO-2011/01/B/ST7/00666.
文摘A simple model of the phase-detection autofocus device based on the partially masked sensor pixels is described. The cross-correlation function of the half-images registered by the masked pixels is proposed as a focus function. It is shown that—in such setting—focusing is equivalent to searching of the cross-correlation function maximum. Application of stochastic approximation algorithms to unimodal and non-unimodal focus functions is shortly discussed.
文摘The phase error estimated by phase gradient autofocus(PGA) is not based on a finite order polynomial mode, so PGA has a good autofocus property for arbitrary order phase error and is fit for high resolution airborne SAR. But PGA has two shortcomings: first, it has a worse estimation property for fast changing phase error; second, there exists a section of linear phase in the phase error estimated by this algorithm. This paper introduces the idea of rank one phase estimate (ROPE) autofocus technique, and improves PGA. The improved PGA(IPGA) can successfully overcome both these shortcomings of PGA.
文摘A new approach to phase averaging in phase gradient algorithm (PGA) is proposed, which is based on the fundamental fact that the information of translational phase error is widely contained in every defocused scatterer in ISAR image. The new approach aims to choose strong scatterers for error phase averaging with a threshold rather than just simply to pick out the strongest point in each range cell, which is not necessarily real strong scatterers if in some range cells consists more than one strong scatterer and whereas in other range cells no scatterer at all. The results of processing real data are presented to confirm the validity of the proposed approach.
基金Supported by National Natural Science Foundation of China (No50305026)Open Foundation of Guangxi Key Lab for Manufacturing Systems and Advanced Manufacturing Technology (No07109008-025-K)
文摘Autofocus method based on the analysis of image content information is investigated to reduce the alignment error resulting from mark positioning uncertainty due to defocus in microstructure layered fabrication process based on multilevel imprint lithography. The applicability of several autofocus functions to the alignment mark images is evaluated concerning their uniformity, sharpness near peak, reliability and measure computation efficiency and the most suitable one based on power spectrum in frequency domain (PSFD) is adopted. To solve the problem of too much computation amount needed in PSFD algorithm, the strategy of interested region detection and effective image reconstruction is proposed and the algorithm efficiency is improved. The test results show that the computation time is reduced from 0.316 s to 0.023 s under the same conditions while the other merits of the function are preserved, which indicates that the modified algorithm can meet the mark image autofocusing requirements in response time, accuracy and robustness. The alignment error due to defocus which is about 0.5 μm indicated by experimental results can be reduced or eliminated by the autofocusing implementation.
基金Project supported by the National Natural Science Foundation of China (Grant No. 11604058)Guangxi Natural Science Foundation (Grant Nos. 2020GXNSFAA297041 and 2023JJA110112)+1 种基金Innovation Project of Guangxi Graduate Education (Grant No. YCSW2023083)Sichuan Science and Technology Program (Grant No. 2023NSFSC0460)。
文摘Turbulence in complex environments such as the atmosphere and biological media has always been a great challenge to the application of beam propagation in optical communication, optical trapping and manipulation. To overcome this challenge, this study comprehensively investigates the robust propagation of traditional Gaussian and autofocusing beams in turbulent environments. In order to select stable beams that exhibit high intensity and high field gradient at the focal position in complex environments, Kolmogorov turbulence theory is used to simulate the propagation of beams in atmospheric turbulence based on the multi-phase screen method. We systematically analyze the intensity fluctuations, the variation of the coherence factor and the change in the scintillation index with propagation distance. The analysis reveals that the intensity fluctuations of autofocusing beams are significantly smaller than those of Gaussian beams, and the coherence of autofocusing beams is better than that of Gaussian beams under turbulence. Moreover, autofocusing beams exhibit less oscillation than Gaussian beams, indicating that autofocusing beams propagate in complex environments with less distortion and intensity fluctuation. Overall, this work clearly demonstrates that autofocusing beams exhibit higher stability in propagation compared with Gaussian beams, showing great promise for applications such as optical trapping and manipulation in complex environments.
文摘This paper proposes an autofocus algorithm used for Synthetic Aperture Radar (SAR) images, called Adaptive Kurtosis Optimization Autofocus Algorithm (AKOAA). The AKOAA can reduce the differ-ence between initial value and real value in focusing by adaptively adjusting the initial value, therefore effec-tively improve the local extremum problem in the Contrast Optimization Autofocus Algorithm (COAA) and speed up the convergence velocity. The principle and realization method of AKOAA are thoroughly investi-gated, and experimental results using real L-band SAR data show that the focus speed of AKOAA is nearly doubled compared with that of the COAA, and the image contrast of AKOAA is improved as well.
基金supported by Thailand Research Fund and Solimac Automation Co.,Ltd.under the TRF Master Research(TRF-MAG)under Grant No.MRG555E058supported by National Research University Project of Thailand
文摘In this paper,we present a high speed autofocus system for micro system applications and design a look-up-table based autofocusing algorithm for applications when a target object is always visible,e.g.,manufacturing parts with alignment fiducials.We perform an evaluation of 24 focus measures to verify that which focus measure is the best for the look-up-table based method.From the evaluation,we find that the Chebyshev moments-based focus measure（CHEB） is the most suitable.Furthermore,we also develop a look-up-table based autofocus system that uses CHEB as the focus measure.In training phase,we offline construct a table from training images of an object that are captured at several lens distances.Each entry of table consists of focus measure computed from image and lens distance.In working phase,given an input image,the algorithm first computes the focus measure and then finds the best match focus measure from the table and looks up the corresponding lens position for moving it into the in-focus position.Our algorithm can perform autofocusing within only 2 steps of lens moving.The experiment shows that the system can perform high speed autofocusing of micro objects.
