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.

Embedding a Signature in an Image Based on Fractal Compress Transformations

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Inversion of receiver function by wavelet transformation

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.

First Order Fuzzy Transform for Images Compression

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.

结合Transformer与生成对抗网络的水下图像增强算法

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。

Diffraction separation and imaging based on double sparse transforms

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.

An ICPSO-RBFNN nonlinear inversion for electrical resistivity imaging

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.

A new approach for inversion of receiver function for crustal structure in the depth domain

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.

Cone-beam local reconstruction based on a Radon inversion transformation

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.

Anisotropic Total Variation Regularization Based NAS-RIF Blind Restoration Method for OCT Image

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.

Adaptive Enhancement Techniques for Solar Images

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.

Inverse Computation and the Universal Resolving Algorithm

We survey fundamental concepts for inverse programming and then present the Universal Resolving Algorithm, an algorithm for inverse computation in a first order, functional programming language. We discuss the key concepts of the algorithm, including a three step approach based on the notion of a perfect process tree, and demonstrate our implementation with several examples of inverse computation.

COMPREHENSIVE INTEGRATION METHODS AND APPLICATION OF GRAVITY-MAGNETIC DATA IMAGE

the technique of image processing and analysis of gravity and magnetic data is one of themost effective ways to extract geological information from gravity and msanetic data. The presentpaper investigates, from an angle of generalized joint inversion, thc methods and procedures ofcomprehensive processing of multi-source geological image , and a specific example in Huai Nan coalfield is given here as well.

ADAPTIVE RECONSTRUCTION TECHNIQUE FOR THE LOST INFORMATION OF THE RECTANGULAR IMAGE AREA

The adaptive reconstruction for the lost information of the rectangular image area is very important for the robust transmission and restoration of the image. In this paper, a new reconstruction method based on the Discrete Cosine Transform (DCT) domain has been put forward. According to the low pass character of the human visual system and the energy distribution of the DCT coefficients on the rectangular boundary, the DCT coefficients of the rectangular image area are adaptively selected and recovered. After the Inverse Discrete Cosine Transform (IDCT), the lost information of the rectangular image area can be reconstructed. The experiments have demonstrated that the subjective and objective qualities of the reconstructed images are enhanced greatly than before.

On q^2-Trigonometric Functions and Their q2-Fourier Transform

In this paper,we first construct generalized q^2-cosine,q^2-sine and q^2-exponential functions.We then use q^2-exponential function in order to define and investigate a q^2-Fourier transform.We establish q-analogues of inversion and Plancherel theorems.

Arterial spin labeling in neuroimaging

Arterial spin labeling(ASL) is a magnetic resonance imaging technique for measuring tissue perfusion using a freely diffusible intrinsic tracer.As compared with other perfusion techniques,ASL offers several advantages and is now available for routine clinical practice in many institutions.Its noninvasive nature and ability to quantitatively measure tissue perfusion make ASL ideal for research and clinical studies.Recent technical advances have increased its sensitivity and also extended its potential applications.This review focuses on some basic knowledge of ASL perfusion,emerging techniques and clinical applications in neuroimaging.

Transformer网络在大地电磁反演成像中的应用

传统大地电磁反演通常是基于确定性梯度的迭代求解,不仅需要大量时间计算雅可比矩阵,还依赖于初始模型的输入和正则化因子等参数的设置。近年来学者们不断引入机器学习方法以试图改善大地电磁反演,该方法不需要计算雅可比矩阵,不用输入初始模型,训练好的网络仅需几毫秒就可实现反演成像。这里利用Google团队提出的Transformer神经网络经典框架搭建大地电磁数据和模型之间的映射网络,以9240组正演数据为样本,对Transformer网络参数进行训练。采用南非开源大地电磁数据,实现了由视电阻率图像到电阻率模型的反演成像。研究表明:①经训练后的Transformer网络可以较准确的反映出异常体位置和大小;②网络实现了简单的矩阵并行化运算,大幅度提高训练的效率,且成像效率高于传统的反演。

Brain functional network connectivity based on a visual task: visual information processing-related brain regions are significantly activated in the task state

It is not clear whether the method used in functional brain-network related research can be applied to explore the feature binding mechanism of visual perception. In this study, we inves-tigated feature binding of color and shape in visual perception. Functional magnetic resonance imaging data were collected from 38 healthy volunteers at rest and while performing a visual perception task to construct brain networks active during resting and task states. Results showed that brain regions involved in visual information processing were obviously activated during the task. The components were partitioned using a greedy algorithm, indicating the visual network existed during the resting state.Z-values in the vision-related brain regions were calculated, conifrming the dynamic balance of the brain network. Connectivity between brain regions was determined, and the result showed that occipital and lingual gyri were stable brain regions in the visual system network, the parietal lobe played a very important role in the binding process of color features and shape features, and the fusiform and inferior temporal gyri were crucial for processing color and shape information. Experimental ifndings indicate that understanding visual feature binding and cognitive processes will help establish computational models of vision, improve image recognition technology, and provide a new theoretical mechanism for feature binding in visual perception.

Resolution Increase and Noise Removal in Particle Size Distribution Measurement with Shifrin Transform

Based on the laser diffraction and Shifrin transform,the measurement method of particle size distribution has been improved extensively.While in real measurements,some noise peaks exist in the inversion data and are easily to be misread as particle distribution peaks.The improved method used a truncation function as a filter is hard to distinguish adjacent peaks.Here,by introducing the bimodal resolution criterion,the filter function is optimized,and to a quasi truncation function with the optimized filter function is studied to achieve optimal bimodal resolution and to remove noise peaks.This new quasi truncation function fits multimode distribution very well.By combining the quasi truncation function with Shifrin transform,noise peaks are removed well and the adjacent peaks are distinguished clearly.Finally,laser diffraction experiments are conducted and the particle size distribution is analyzed by adoping the method.The results show that the quasi truncation function has better bimodal resolution than the truncation function.Generally,by combining the quasi truncation function with the Shifrin transform,in particle size distribution measurements with laser diffraction,the bimodal resolution is greatly increased and the noise is removed well.And the results can restore the original distribution perfectly.Therefore,the new method with combination of the quasi truncation function and the Shifrin transform provides a feasible and effective way to measure the multimode particle size distribution by laser diffraction.

Rotational parameters estimation of maneuvering target in ISAR imaging

The rotational parameters estimation of maneuvering target is the key of cross-range scaling of ISAR （inverse synthetic aperture radar）, which can be used in the target feature extraction. The cross-range signal model of rotating target with fixed acceleration is presented and the weighted linear least squares estimation of rotational parameters with fixed velocity or acceleration is proposed via the relationship of cross-range FM （frequency modulation） parameter, scatterers coordinates and rotational parameters. The FM parameter is calculated via RWT （Radon-Wigner transform）. The ISAR imaging and cross-range scaling based on scaled RWT imaging method are implemented after obtaining rotational parameters. The rotational parameters estimation and cross-range scaling are validated by the ISAR processing of experimental radar data, and the method presents good application foreground to the ISAR imaging and scaling of maneuvering target.