This paper introduces a novel method for medical image retrieval and classification by integrating a multi-scale encoding mechanism with Vision Transformer(ViT)architectures and a dynamic multi-loss function.The multi...This paper introduces a novel method for medical image retrieval and classification by integrating a multi-scale encoding mechanism with Vision Transformer(ViT)architectures and a dynamic multi-loss function.The multi-scale encoding significantly enhances the model’s ability to capture both fine-grained and global features,while the dynamic loss function adapts during training to optimize classification accuracy and retrieval performance.Our approach was evaluated on the ISIC-2018 and ChestX-ray14 datasets,yielding notable improvements.Specifically,on the ISIC-2018 dataset,our method achieves an F1-Score improvement of+4.84% compared to the standard ViT,with a precision increase of+5.46% for melanoma(MEL).On the ChestX-ray14 dataset,the method delivers an F1-Score improvement of 5.3%over the conventional ViT,with precision gains of+5.0% for pneumonia(PNEU)and+5.4%for fibrosis(FIB).Experimental results demonstrate that our approach outperforms traditional CNN-based models and existing ViT variants,particularly in retrieving relevant medical cases and enhancing diagnostic accuracy.These findings highlight the potential of the proposedmethod for large-scalemedical image analysis,offering improved tools for clinical decision-making through superior classification and case comparison.展开更多
In this paper, we estimate the Fekete-Szego functional with k-th root transform for the inverse of certain classes of analytic univalent functions using quasi-subordination.
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 qua...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.展开更多
目前自闭症功能磁共振(functional magnetic resonance imaging,fMRI)图像分类模型在跨多个机构的数据集下分类精度较低,难以应用到自闭症的诊断工作中。为此,本文提出了一种基于Transformer的自闭症分类模型(autism spectrum disorder ...目前自闭症功能磁共振(functional magnetic resonance imaging,fMRI)图像分类模型在跨多个机构的数据集下分类精度较低,难以应用到自闭症的诊断工作中。为此,本文提出了一种基于Transformer的自闭症分类模型(autism spectrum disorder classification model based on Transformer,TransASD)。首先采用脑图谱模板提取fMRI数据中的时间序列输入Transformer模型,并引入一种重叠窗口注意力机制,能够更好地捕捉异构数据的局部与全局特征。其次,提出了一个跨窗口正则化方法作为额外的损失项,使模型可以更加准确地聚焦于重要的特征。本文使用该模型在公开的自闭症数据集ABIDE上进行实验,在10折交叉验证法下得到了71.44%的准确率,该模型对比其他先进算法模型取得了更好的分类效果。展开更多
城市功能区识别可为城市建设决策提供技术支撑。本研究提出了面向城市功能区识别的多源场景特征Transformer融合方法。利用路网构建交通分析区(Traffic Analysis Zone,TAZ),采用Delaunay三角网创建POI(Point of Interest)数据的图结构,...城市功能区识别可为城市建设决策提供技术支撑。本研究提出了面向城市功能区识别的多源场景特征Transformer融合方法。利用路网构建交通分析区(Traffic Analysis Zone,TAZ),采用Delaunay三角网创建POI(Point of Interest)数据的图结构,通过TAZ获得遥感数据的影像对象;利用图卷积网络提取POI图结构的社会场景特征,由ResNet-50编码遥感数据的自然场景特征;基于Transformer的多头注意力机制融合多维特征,依托SoftMax实现功能区识别。以沈阳市主城区为例,以2021年的OpenStreetMap、POI和遥感数据为数据源。该方法的总体精度和Kappa系数为82.2%和70%,Kappa系数较单一数据方法和其他融合方法至少提高18%和9%。本研究采用Transformer融合社会场景特征和自然场景特征,解决了多源数据难以集成表达的问题,为城市功能区识别提供了新的技术路径。展开更多
A new method for receiver function inversion by wavelet transformation is presented in this paper. Receiver func-tion is expanded to different scales with different resolution by wavelet transformation. After an initi...A new method for receiver function inversion by wavelet transformation is presented in this paper. Receiver func-tion is expanded to different scales with different resolution by wavelet transformation. After an initial model be-ing taken, a generalized least-squares inversion procedure is gradually carried out for receiver function from low to high scale, with the inversion result for low order receiver function as the initial model for high order. A neighborhood containing the global minimum is firstly searched from low scale receiver function, and will gradu-ally focus at the global minimum by introducing high scale information of receiver function. With the gradual ad-dition of high wave-number to smooth background velocity structure, wavelet transformation can keep the inver-sion result converge to the global minimum, reduce to certain extent the dependence of inversion result on the initial model, overcome the nonuniqueness of generalized least-squares inversion, and obtain reliable crustal and upper mantle velocity with high resolution.展开更多
In this paper, we present a new image compression method based on the direct and inverse F1-transform, a generalization of the concept of fuzzy transform. Under weak compression rates, this method improves the quality...In this paper, we present a new image compression method based on the direct and inverse F1-transform, a generalization of the concept of fuzzy transform. Under weak compression rates, this method improves the quality of the images with respect to the classical method based on the fuzzy transform.展开更多
由于水下环境的多样性和光在水中受到的散射及选择性吸收作用,采集到的水下图像通常会产生严重的质量退化问题,如颜色偏差、清晰度低和亮度低等,为解决以上问题,本文提出了一种基于Transformer和生成对抗网络的水下图像增强算法。以生...由于水下环境的多样性和光在水中受到的散射及选择性吸收作用,采集到的水下图像通常会产生严重的质量退化问题,如颜色偏差、清晰度低和亮度低等,为解决以上问题,本文提出了一种基于Transformer和生成对抗网络的水下图像增强算法。以生成对抗网络为基础架构,结合编码解码结构、基于空间自注意力机制的全局特征建模Transformer模块和通道级多尺度特征融合Transformer模块构建了TGAN(generative adversarial network with transformer)网络增强模型,重点关注水下图像衰减更严重的颜色通道和空间区域,有效增强了图像细节并解决了颜色偏差问题。此外,设计了一种结合RGB和LAB颜色空间的多项损失函数,约束网络增强模型的对抗训练。实验结果表明,与CLAHE(contrast limited adaptive histogram equalization)、UDCP(underwater dark channel prior)、UWCNN(underwater based on convolutional neural network)、FUnIE-GAN(fast underwater image enhancement for improved visual perception)等典型水下图像增强算法相比,所提算法增强后的水下图像在清晰度、细节纹理和色彩表现等方面都有所提升,客观评价指标如峰值信噪比、结构相似性和水下图像质量度量的平均值分别提升了5.8%、1.8%和3.6%,有效地提升了水下图像的视觉感知效果。展开更多
Reflection imaging results generally reveal large-scale continuous geological information,and it is difficult to identify small-scale geological bodies such as breakpoints,pinch points,small fault blocks,caves,and fra...Reflection imaging results generally reveal large-scale continuous geological information,and it is difficult to identify small-scale geological bodies such as breakpoints,pinch points,small fault blocks,caves,and fractures,etc.Diffraction imaging is an important method to identify small-scale geological bodies and it has higher resolution than reflection imaging.In the common-offset domain,reflections are mostly expressed as smooth linear events,whereas diffractions are characterized by hyperbolic events.This paper proposes a diffraction extraction method based on double sparse transforms.The linear events can be sparsely expressed by the high-resolution linear Radon transform,and the curved events can be sparsely expressed by the Curvelet transform.A sparse inversion model is built and the alternating direction method is used to solve the inversion model.Simulation data and field data experimental results proved that the diffractions extraction method based on double sparse transforms can effectively improve the imaging quality of faults and other small-scale geological bodies.展开更多
For the case of atomic force microscope (AFM) automation, we extract the most valuable sub-region of a given AFM image automatically for succeeding scanning to get the higher resolution of interesting region. Two obje...For the case of atomic force microscope (AFM) automation, we extract the most valuable sub-region of a given AFM image automatically for succeeding scanning to get the higher resolution of interesting region. Two objective functions are sum- marized based on the analysis of evaluation of the information of a sub-region, and corresponding algorithm principles based on standard deviation and Discrete Cosine Transform (DCT) compression are determined from math. Algorithm realizations are analyzed and two selec...展开更多
To improve the global search ability and imaging quality of electrical resistivity imaging(ERI) inversion, a two-stage learning ICPSO algorithm of radial basis function neural network(RBFNN) based on information crite...To improve the global search ability and imaging quality of electrical resistivity imaging(ERI) inversion, a two-stage learning ICPSO algorithm of radial basis function neural network(RBFNN) based on information criterion(IC) and particle swarm optimization(PSO) is presented. In the proposed method, IC is applied to obtain the hidden layer structure by calculating the optimal IC value automatically and PSO algorithm is used to optimize the centers and widths of the radial basis functions in the hidden layer. Meanwhile, impacts of different information criteria to the inversion results are compared, and an implementation of the proposed ICPSO algorithm is given. The optimized neural network has one hidden layer with 261 nodes selected by AKAIKE's information criterion(AIC) and it is trained on 32 data sets and tested on another 8 synthetic data sets. Two complex synthetic examples are used to verify the feasibility and effectiveness of the proposed method with two learning stages. The results show that the proposed method has better performance and higher imaging quality than three-layer and four-layer back propagation neural networks(BPNNs) and traditional least square(LS) inversion.展开更多
A method for reconstructing crustal velocity structure using the optimization of stacking receiver function amplitude in the depth domain,named common conversion amplitude(CCA)inversion,is presented.The conversion amp...A method for reconstructing crustal velocity structure using the optimization of stacking receiver function amplitude in the depth domain,named common conversion amplitude(CCA)inversion,is presented.The conversion amplitude in the depth domain,which represents the impedance change in the medium,is obtained by assigning the receiver function amplitude to the corresponding conversion position where the P-to-S conversion occurred.Utilizing the conversion amplitude variation with depth as an optimization objective,imposing reliable prior constraints on the structural model frame and velocity range,and adopting a stepwise search inversion technique,this method efficiently weakens the tendency of easily falling into the local extremum in conventional receiver function inversion.Synthetic tests show that the CCA inversion can reconstruct complex crustal velocity structures well and is especially suitable for revealing crustal evolution by estimating diverse velocity distributions.Its performance in reconstructing crustal structure is superior to that of the conventional receiver function imaging method.展开更多
The local reconstruction from truncated projection data is one area of interest in image reconstruction for com- puted tomography （CT）, which creates the possibility for dose reduction. In this paper, a filtered-bac...The local reconstruction from truncated projection data is one area of interest in image reconstruction for com- puted tomography （CT）, which creates the possibility for dose reduction. In this paper, a filtered-backprojection （FBP） algorithm based on the Radon inversion transform is presented to deal with the three-dimensional （3D） local recon- struction in the circular geometry. The algorithm achieves the data filtering in two steps. The first step is the derivative of projections, which acts locally on the data and can thus be carried out accurately even in the presence of data trun- cation. The second step is the nonlocal Hilbert filtering. The numerical simulations and the real data reconstructions have been conducted to validate the new reconstruction algorithm. Compared with the approximate truncation resistant algorithm for computed tomography （ATRACT）, not only it has a comparable ability to restrain truncation artifacts, but also its reconstruction efficiency is improved. It is about twice as fast as that of the ATRACT. Therefore, this work provides a simple and efficient approach for the approximate reconstruction from truncated projections in the circular cone-beam CT.展开更多
This paper presents a wavelet-based hybrid threshold method according to the soft- and hard-threshold functions proposed by Donoho. The wavelet-based hybrid threshold method may help doctors to know more details on th...This paper presents a wavelet-based hybrid threshold method according to the soft- and hard-threshold functions proposed by Donoho. The wavelet-based hybrid threshold method may help doctors to know more details on the liver disease through denoising the ultrasound image of the liver. First of all, an analytical expression for the hybrid threshold function is discussed. The wavelet-based hybrid threshold method is then investigated for ultrasound image of the liver. Finally, we test the influence of this parameter on the proposed method with the real ultrasound image corrupted by speckle noise with different variances. Moreover, we compare the proposed method under the varying parameters with the soft-threshold function and the hard-threshold function. Three metrics, which are correlation coefficient, edge preservation index and structural similarity index, are measured to quantify the denoised results of ultrasound liver image. Experimental results demonstrate the potential of the proposed method for ultrasound liver image denosing.展开更多
Based on anisotropic total variation regularization(ATVR), a nonnegativity and support constraints recursive inverse filtering(NAS-RIF) blind restoration method is proposed to enhance the quality of optical coherence ...Based on anisotropic total variation regularization(ATVR), a nonnegativity and support constraints recursive inverse filtering(NAS-RIF) blind restoration method is proposed to enhance the quality of optical coherence tomography(OCT) image. First, ATVR is introduced into the cost function of NAS-RIF to improve the noise robustness and retain the details in the image.Since the split Bregman iterative is used to optimize the ATVR based cost function, the ATVR based NAS-RIF blind restoration method is then constructed. Furthermore, combined with the geometric nonlinear diffusion filter and the Poisson-distribution-based minimum error thresholding, the ATVR based NAS-RIF blind restoration method is used to realize the blind OCT image restoration. The experimental results demonstrate that the ATVR based NAS-RIF blind restoration method can successfully retain the details in the OCT images. In addition, the signal-to-noise ratio of the blind restored OCT images can be improved, along with the noise robustness.展开更多
Radio astronomy radio telescope plays the role of a linear operator, affecting the function that describes the object of research, formation of image of a monitored object. This paper presents methods for reconstructi...Radio astronomy radio telescope plays the role of a linear operator, affecting the function that describes the object of research, formation of image of a monitored object. This paper presents methods for reconstruction and correction of solar radio images using the algorithm of rejections, the updated Weiner-filter, and the method CLEAN designed by Hegbomom (Pseudonym, 2009) for point sources. It is the process of numerical convolution in signal handling, an algorithm for separating weak-contrast formations on the solar which represents most points of the actual limb by using the ellipse equation. Consequently, the filling algorithm is applied by moving from the center to the ellipse points and filling each point by solar image data. Finally, a linear limb-darkening expression is used to remove the limb darkening. Different examples of the intermediate and final results are presented in addition to the developed algorithm.展开更多
基金funded by the Deanship of Research and Graduate Studies at King Khalid University through small group research under grant number RGP1/278/45.
文摘This paper introduces a novel method for medical image retrieval and classification by integrating a multi-scale encoding mechanism with Vision Transformer(ViT)architectures and a dynamic multi-loss function.The multi-scale encoding significantly enhances the model’s ability to capture both fine-grained and global features,while the dynamic loss function adapts during training to optimize classification accuracy and retrieval performance.Our approach was evaluated on the ISIC-2018 and ChestX-ray14 datasets,yielding notable improvements.Specifically,on the ISIC-2018 dataset,our method achieves an F1-Score improvement of+4.84% compared to the standard ViT,with a precision increase of+5.46% for melanoma(MEL).On the ChestX-ray14 dataset,the method delivers an F1-Score improvement of 5.3%over the conventional ViT,with precision gains of+5.0% for pneumonia(PNEU)and+5.4%for fibrosis(FIB).Experimental results demonstrate that our approach outperforms traditional CNN-based models and existing ViT variants,particularly in retrieving relevant medical cases and enhancing diagnostic accuracy.These findings highlight the potential of the proposedmethod for large-scalemedical image analysis,offering improved tools for clinical decision-making through superior classification and case comparison.
基金Supported by the National Natural Science Foundation of China (Grant No. 11561001)the Natural Science Foundation of Inner Mongolia of China (Grant No. 2018MS01026)+4 种基金the Natural Science Foundation of Anhui Provincial Department of Education (Grant Nos. KJ2018A0833KJ2020A0993KJ2020ZD74)Provincial Quality Engineering Project of Anhui Colleges and Universities (Grant No. 2018mooc608)the Key Cultivated Project at School Level of the National Science Fund of Guangzhou Civil Aviation College (Grant No. 18X0428)。
文摘In this paper, we estimate the Fekete-Szego functional with k-th root transform for the inverse of certain classes of analytic univalent functions using quasi-subordination.
基金sponsored by the National Key R&D Plan Project(Grant No.2016YFC0303900)Natural Science Foundation of China(Grant No.41374145)
文摘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.
文摘目前自闭症功能磁共振(functional magnetic resonance imaging,fMRI)图像分类模型在跨多个机构的数据集下分类精度较低,难以应用到自闭症的诊断工作中。为此,本文提出了一种基于Transformer的自闭症分类模型(autism spectrum disorder classification model based on Transformer,TransASD)。首先采用脑图谱模板提取fMRI数据中的时间序列输入Transformer模型,并引入一种重叠窗口注意力机制,能够更好地捕捉异构数据的局部与全局特征。其次,提出了一个跨窗口正则化方法作为额外的损失项,使模型可以更加准确地聚焦于重要的特征。本文使用该模型在公开的自闭症数据集ABIDE上进行实验,在10折交叉验证法下得到了71.44%的准确率,该模型对比其他先进算法模型取得了更好的分类效果。
文摘城市功能区识别可为城市建设决策提供技术支撑。本研究提出了面向城市功能区识别的多源场景特征Transformer融合方法。利用路网构建交通分析区(Traffic Analysis Zone,TAZ),采用Delaunay三角网创建POI(Point of Interest)数据的图结构,通过TAZ获得遥感数据的影像对象;利用图卷积网络提取POI图结构的社会场景特征,由ResNet-50编码遥感数据的自然场景特征;基于Transformer的多头注意力机制融合多维特征,依托SoftMax实现功能区识别。以沈阳市主城区为例,以2021年的OpenStreetMap、POI和遥感数据为数据源。该方法的总体精度和Kappa系数为82.2%和70%,Kappa系数较单一数据方法和其他融合方法至少提高18%和9%。本研究采用Transformer融合社会场景特征和自然场景特征,解决了多源数据难以集成表达的问题,为城市功能区识别提供了新的技术路径。
基金National Nature Science Foundation of China (49974021).
文摘A new method for receiver function inversion by wavelet transformation is presented in this paper. Receiver func-tion is expanded to different scales with different resolution by wavelet transformation. After an initial model be-ing taken, a generalized least-squares inversion procedure is gradually carried out for receiver function from low to high scale, with the inversion result for low order receiver function as the initial model for high order. A neighborhood containing the global minimum is firstly searched from low scale receiver function, and will gradu-ally focus at the global minimum by introducing high scale information of receiver function. With the gradual ad-dition of high wave-number to smooth background velocity structure, wavelet transformation can keep the inver-sion result converge to the global minimum, reduce to certain extent the dependence of inversion result on the initial model, overcome the nonuniqueness of generalized least-squares inversion, and obtain reliable crustal and upper mantle velocity with high resolution.
文摘In this paper, we present a new image compression method based on the direct and inverse F1-transform, a generalization of the concept of fuzzy transform. Under weak compression rates, this method improves the quality of the images with respect to the classical method based on the fuzzy transform.
文摘由于水下环境的多样性和光在水中受到的散射及选择性吸收作用,采集到的水下图像通常会产生严重的质量退化问题,如颜色偏差、清晰度低和亮度低等,为解决以上问题,本文提出了一种基于Transformer和生成对抗网络的水下图像增强算法。以生成对抗网络为基础架构,结合编码解码结构、基于空间自注意力机制的全局特征建模Transformer模块和通道级多尺度特征融合Transformer模块构建了TGAN(generative adversarial network with transformer)网络增强模型,重点关注水下图像衰减更严重的颜色通道和空间区域,有效增强了图像细节并解决了颜色偏差问题。此外,设计了一种结合RGB和LAB颜色空间的多项损失函数,约束网络增强模型的对抗训练。实验结果表明,与CLAHE(contrast limited adaptive histogram equalization)、UDCP(underwater dark channel prior)、UWCNN(underwater based on convolutional neural network)、FUnIE-GAN(fast underwater image enhancement for improved visual perception)等典型水下图像增强算法相比,所提算法增强后的水下图像在清晰度、细节纹理和色彩表现等方面都有所提升,客观评价指标如峰值信噪比、结构相似性和水下图像质量度量的平均值分别提升了5.8%、1.8%和3.6%,有效地提升了水下图像的视觉感知效果。
基金supported by National Natural Science Foundation of China(41974166)Natural Science Foundation of Hebei Province(D2019403082,D2021403010)+1 种基金Hebei Province“three-threethree talent project”(A202005009)Funding for the Science and Technology Innovation Team Project of Hebei GEO University(KJCXTD202106)
文摘Reflection imaging results generally reveal large-scale continuous geological information,and it is difficult to identify small-scale geological bodies such as breakpoints,pinch points,small fault blocks,caves,and fractures,etc.Diffraction imaging is an important method to identify small-scale geological bodies and it has higher resolution than reflection imaging.In the common-offset domain,reflections are mostly expressed as smooth linear events,whereas diffractions are characterized by hyperbolic events.This paper proposes a diffraction extraction method based on double sparse transforms.The linear events can be sparsely expressed by the high-resolution linear Radon transform,and the curved events can be sparsely expressed by the Curvelet transform.A sparse inversion model is built and the alternating direction method is used to solve the inversion model.Simulation data and field data experimental results proved that the diffractions extraction method based on double sparse transforms can effectively improve the imaging quality of faults and other small-scale geological bodies.
基金supported by the National High TechnologyResearch and Development Program of China (Grant No.2007AA122128)
文摘For the case of atomic force microscope (AFM) automation, we extract the most valuable sub-region of a given AFM image automatically for succeeding scanning to get the higher resolution of interesting region. Two objective functions are sum- marized based on the analysis of evaluation of the information of a sub-region, and corresponding algorithm principles based on standard deviation and Discrete Cosine Transform (DCT) compression are determined from math. Algorithm realizations are analyzed and two selec...
基金Project(41374118)supported by the National Natural Science Foundation,ChinaProject(20120162110015)supported by Research Fund for the Doctoral Program of Higher Education,China+3 种基金Project(2015M580700)supported by the China Postdoctoral Science Foundation,ChinaProject(2016JJ3086)supported by the Hunan Provincial Natural Science Foundation,ChinaProject(2015JC3067)supported by the Hunan Provincial Science and Technology Program,ChinaProject(15B138)supported by the Scientific Research Fund of Hunan Provincial Education Department,China
文摘To improve the global search ability and imaging quality of electrical resistivity imaging(ERI) inversion, a two-stage learning ICPSO algorithm of radial basis function neural network(RBFNN) based on information criterion(IC) and particle swarm optimization(PSO) is presented. In the proposed method, IC is applied to obtain the hidden layer structure by calculating the optimal IC value automatically and PSO algorithm is used to optimize the centers and widths of the radial basis functions in the hidden layer. Meanwhile, impacts of different information criteria to the inversion results are compared, and an implementation of the proposed ICPSO algorithm is given. The optimized neural network has one hidden layer with 261 nodes selected by AKAIKE's information criterion(AIC) and it is trained on 32 data sets and tested on another 8 synthetic data sets. Two complex synthetic examples are used to verify the feasibility and effectiveness of the proposed method with two learning stages. The results show that the proposed method has better performance and higher imaging quality than three-layer and four-layer back propagation neural networks(BPNNs) and traditional least square(LS) inversion.
基金financially supported by the National Natural Science Foundation of China(Grant 91755214).
文摘A method for reconstructing crustal velocity structure using the optimization of stacking receiver function amplitude in the depth domain,named common conversion amplitude(CCA)inversion,is presented.The conversion amplitude in the depth domain,which represents the impedance change in the medium,is obtained by assigning the receiver function amplitude to the corresponding conversion position where the P-to-S conversion occurred.Utilizing the conversion amplitude variation with depth as an optimization objective,imposing reliable prior constraints on the structural model frame and velocity range,and adopting a stepwise search inversion technique,this method efficiently weakens the tendency of easily falling into the local extremum in conventional receiver function inversion.Synthetic tests show that the CCA inversion can reconstruct complex crustal velocity structures well and is especially suitable for revealing crustal evolution by estimating diverse velocity distributions.Its performance in reconstructing crustal structure is superior to that of the conventional receiver function imaging method.
基金Project supported by the National High Technology Research and Development Program of China (Grant No. 2012AA011603)
文摘The local reconstruction from truncated projection data is one area of interest in image reconstruction for com- puted tomography （CT）, which creates the possibility for dose reduction. In this paper, a filtered-backprojection （FBP） algorithm based on the Radon inversion transform is presented to deal with the three-dimensional （3D） local recon- struction in the circular geometry. The algorithm achieves the data filtering in two steps. The first step is the derivative of projections, which acts locally on the data and can thus be carried out accurately even in the presence of data trun- cation. The second step is the nonlocal Hilbert filtering. The numerical simulations and the real data reconstructions have been conducted to validate the new reconstruction algorithm. Compared with the approximate truncation resistant algorithm for computed tomography （ATRACT）, not only it has a comparable ability to restrain truncation artifacts, but also its reconstruction efficiency is improved. It is about twice as fast as that of the ATRACT. Therefore, this work provides a simple and efficient approach for the approximate reconstruction from truncated projections in the circular cone-beam CT.
基金the Fundamental Research Funds for the Central Universities of China(No.YS1404)the Beijing University of Chemical Technology Interdisciplinary Funds for "Visual Media Computing"
文摘This paper presents a wavelet-based hybrid threshold method according to the soft- and hard-threshold functions proposed by Donoho. The wavelet-based hybrid threshold method may help doctors to know more details on the liver disease through denoising the ultrasound image of the liver. First of all, an analytical expression for the hybrid threshold function is discussed. The wavelet-based hybrid threshold method is then investigated for ultrasound image of the liver. Finally, we test the influence of this parameter on the proposed method with the real ultrasound image corrupted by speckle noise with different variances. Moreover, we compare the proposed method under the varying parameters with the soft-threshold function and the hard-threshold function. Three metrics, which are correlation coefficient, edge preservation index and structural similarity index, are measured to quantify the denoised results of ultrasound liver image. Experimental results demonstrate the potential of the proposed method for ultrasound liver image denosing.
基金Supported by National Key Research and Development Program of China(2016YFF0201005)。
文摘Based on anisotropic total variation regularization(ATVR), a nonnegativity and support constraints recursive inverse filtering(NAS-RIF) blind restoration method is proposed to enhance the quality of optical coherence tomography(OCT) image. First, ATVR is introduced into the cost function of NAS-RIF to improve the noise robustness and retain the details in the image.Since the split Bregman iterative is used to optimize the ATVR based cost function, the ATVR based NAS-RIF blind restoration method is then constructed. Furthermore, combined with the geometric nonlinear diffusion filter and the Poisson-distribution-based minimum error thresholding, the ATVR based NAS-RIF blind restoration method is used to realize the blind OCT image restoration. The experimental results demonstrate that the ATVR based NAS-RIF blind restoration method can successfully retain the details in the OCT images. In addition, the signal-to-noise ratio of the blind restored OCT images can be improved, along with the noise robustness.
文摘Radio astronomy radio telescope plays the role of a linear operator, affecting the function that describes the object of research, formation of image of a monitored object. This paper presents methods for reconstruction and correction of solar radio images using the algorithm of rejections, the updated Weiner-filter, and the method CLEAN designed by Hegbomom (Pseudonym, 2009) for point sources. It is the process of numerical convolution in signal handling, an algorithm for separating weak-contrast formations on the solar which represents most points of the actual limb by using the ellipse equation. Consequently, the filling algorithm is applied by moving from the center to the ellipse points and filling each point by solar image data. Finally, a linear limb-darkening expression is used to remove the limb darkening. Different examples of the intermediate and final results are presented in addition to the developed algorithm.
