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.

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.

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。

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.

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.

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.

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.

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.

可形变Transformer辅助的胸部X光影像疾病诊断模型

针对胸部X光影像中的灰雾现象、病变区域重叠等问题,提出可形变Transformer辅助的胸部X光影像疾病诊断模型.将扩展后的ResNet50作为特征提取网络,添加压缩型双注意力模块,增强病变区域与非病变区域之间的特征差异,降低冗余信息的干扰,提高图像数据的特征提取效果;通过可形变Transformer解码器内部的交叉注意力模块,引入类别表征作为先验知识,引导影像特征进一步融合,提高不同疾病在影像区域重叠情况下的特征区分度;将解码器的输出传入分类器中以获得最终的诊断结果.压缩型双注意力模块和可形变Transformer均起到降低模型计算复杂度的作用,引入非对称损失函数可以更好地解决正负样本不均衡.利用所提模型在公开数据集ChestX-Ray14和CheXpert上进行多组实验,在2个数据集上的受试者操作的特征曲线下面积值(AUC)分别达到0.8398和0.9061,表明该模型在胸部X光影像的疾病诊断方面具有正确性和有效性.

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.

跨级可变形Transformer编解码视网膜图像分割算法

眼底视网膜血管图像分割对青光眼、糖尿病等疾病的预防和诊断具有重要意义。针对视网膜血管图像边缘分割模糊、微细血管漏缺和模型感受野不足等问题,提出了一种跨级可变形Transformer编解码(Cross-stage Deformable Transformer Encoding and Decoding Net,CTED-Net)视网膜图像分割算法。在特征提取网络中融入通道像素增强模块和跨级融合骨干,实现对视网膜血管全局特征的粗提取;在网络编码部分加入可变形自适应编码Transformer模块(Deformable Adaptive Coding Transformer Module,DACT),通过可变形编码的方式增大模型感受野;在编解码结构底层加入深层语义门控注意模块,实现对视网膜血管深层特征的充分学习,以改善血管图像边缘分割模糊的问题。在模型训练阶段采用加权交叉焦点损失函数,克服视网膜血管图像样本不平衡的问题。在公共数据集DRIVE和STARE上进行仿真实验,所提算法灵敏度、特异性、准确率和AUC指标在DRIVE上达到84.25%、98.17%、96.46%和98.70%,在STARE上达到80.22%、98.64%、96.71%和98.78%。通过与其他先进算法对比分析可以看出,所提算法分割效果可靠且整体性能先进。

基于改进切尾均值的矿井图像去噪算法

现有矿井图像去噪算法对于复杂噪声的去除效果有限,且处理速度不能满足实时监控需求。针对该问题,提出一种基于改进切尾均值的矿井图像去噪算法。首先,采用切尾均值滤波器对图像噪声进行初步滤除,同时引入二次检验机制处理残留的噪声点,通过引入离散系数提升算法对不同像素的区分能力,增强去噪性能;其次,采用基于极值数量的分类处理及再次检验机制,有效减少残留噪声问题;然后,在小波函数中引入新的控制变量优化软阈值函数和硬阈值函数,构建双阈值函数,结合Radon变换增强对线性特征的处理,增强对矿井图像的检测能力;最后,采用均方误差(MSE)与峰值信噪比(PSNR)进行图像质量评价。实验结果表明:相较于切尾均值算法、硬阈值算法、软阈值算法,基于改进切尾均值的矿井图像去噪算法处理的图像的MSE增长相对缓慢,MSE最小,图像去噪效果最好;引入离散系数后,去噪图像的MSE相较于引入前低300 dB左右,PSNR相较于引入前高20 dB左右,引入离散系数能有效减少噪声点对算法的影响;相较于卡尔曼遗传优化算法、变换域图像去噪算法、交叉分支卷积去噪网络,基于改进切尾均值的矿井图像去噪算法处理的图像MSE分别降低了27,21,13 dB,PSNR分别提升了8,6,3 dB,去噪耗时分别缩短了0.20,0.16,0.14 s。

基于图像取反的同心圆环半径和圆心提取方法

为了解决非定域干涉同心圆环图像的圆心和半径的精确提取问题,提出了一种基于图像取反的暗亮环兼顾的算法,以点光源非定域干涉同心圆环为对象,在平滑降噪预处理基础上,利用霍夫圆变换获得参考圆心,以参考圆心为基础分别在其x方向和y方向上[-50,50]像素范围内每隔5 pixel取一条强度轮廓线,每一条强度轮廓线取反后,分别实现暗环和亮环峰位坐标的精确识别,再利用圆回归拟合得到系列圆心和半径,通过系列圆心和半径的平均运算实现图像最终圆心和半径的精确提取。结果表明,靠近中心的3个亮环和3个暗环各自的圆心坐标非常接近,相对于总平均值的最大偏差为-3.7 pixel,即相对于总平均值的最大相对偏差为-0.15%,重复性很好;相邻两个圆环半径平方比值相对于实际比值的误差在-4.18%~0.36%之间变化,验证了算法的可行性。该研究对实现自动化检测、提高测量精度是有帮助的。

基于误差函数的复材加筋板概率成像冲击定位

提出了一种复合材料加筋板结构概率成像冲击定位方法。通过在复合材料加筋板结构表面布置稀疏阵列的压电传感器网络接收冲击响应信号,采用复Morlet小波变换提取冲击响应信号特定中心频率的窄带Lamb波信号并计算模值,根据模的峰值获取Lamb波的到达时刻,构建基于波达时间差的误差函数并计算监测区域内各点为冲击源的概率值,将概率值作为像素值对结构进行概率成像冲击定位。在总体尺寸为700 mm×450 mm的碳纤维增强复合材料加筋板结构上对算法进行了验证。实验结果表明,该算法简单有效,成像分辨率和定位精度高,采用较少的传感器数量依然拥有可观的冲击定位精度。

基于指数相位相关性目标函数的全波形反演

全波形反演(FWI)已成为获取浅地表高分辨率S波速度(VS)的有效方法。为了克服FWI在野外应用中的一些挑战,文中引入了一种基于指数相位相关性的目标函数,该函数不依赖于测量和模拟数据的振幅信息。文中采用伴随状态方法高效计算目标函数相对于模型的梯度,并分析了新目标函数的形状。使用受随机噪声污染的模拟数据,证明了该目标函数对随机噪声的鲁棒性。此外,使用在考古遗址上采集的地震数据作为基准数据集,测试了基于指数相位相关性的全波形反演方法在真实野外环境中的性能,同时还添加了随机噪声以进一步评估目标函数对噪声的鲁棒性。文中所得反演结果与该遗址已有的速度结构非常相似,并已通过独立的考古挖掘验证。因此,基于指数相位相关性目标函数的FWI具有在实际野外情况下显著提高浅地表成像准确性的潜力。