This work focuses on 3D Radar imaging inverse problems. Current methods obtain undifferentiated results that suffer task-depended information retrieval loss and thus don't meet the task's specific demands well. For example, biased scattering energy may be acceptable for screen imaging but not for scattering diagnosis. To address this issue, we propose a new task-oriented imaging framework. The imaging principle is task-oriented through an analysis phase to obtain task's demands. The imaging model is multi-cognition regularized to embed and fulfill demands. The imaging method is designed to be general-ized, where couplings between cognitions are decoupled and solved individually with approximation and variable-splitting techniques. Tasks include scattering diagnosis, person screen imaging, and parcel screening imaging are given as examples. Experiments on data from two systems indicate that the pro-posed framework outperforms the current ones in task-depended information retrieval.
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Benefiting from a relatively larger aperture's angle, and in combination with a wide transmitting bandwidth, near-field synthetic aperture radar (SAR) provides a high-resolution image of a target's scattering distribution-hot spots. Meanwhile, imaging result suffers inevitable degradation from sidelobes, clutters, and noises, hindering the information retrieval of the target. To restore the image, current methods make simplified assumptions; for example, the point spread function (PSF) is spatially consistent, the target consists of sparse point scatters, etc. Thus, they achieve limited restoration performance in terms of the target's shape, especially for complex targets. To address these issues, a preliminary study is conducted on restoration with the recent promising deep learning inverse technique in this work. We reformulate the degradation model into a spatially variable complex-convolution model, where the near-field SAR's system response is considered. Adhering to it, a model-based deep learning network is designed to restore the image. A simulated degraded image dataset from multiple complex target models is constructed to validate the network. All the images are formulated using the electromagnetic simulation tool. Experiments on the dataset reveal their effectiveness. Compared with current methods, superior performance is achieved regarding the target's shape and energy estimation.
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Tomographic SAR technique has attracted remarkable interest for its ability of three-dimensional resolving along the elevation direction via a stack of SAR images collected from different cross-track angles. The emerged compressed sensing (CS)-based algorithms have been introduced into TomoSAR considering its super-resolution ability with limited samples. However, the conventional CS-based methods suffer from several drawbacks, including weak noise resistance, high computational complexity, and complex parameter fine-tuning. Aiming at efficient TomoSAR imaging, this paper proposes a novel efficient sparse unfolding network based on the analytic learned iterative shrinkage thresholding algorithm (ALISTA) architecture with adaptive threshold, named Adaptive Threshold ALISTA-based Sparse Imaging Network (ATASI-Net). The weight matrix in each layer of ATASI-Net is pre-computed as the solution of an off-line optimization problem, leaving only two scalar parameters to be learned from data, which significantly simplifies the training stage. In addition, adaptive threshold is introduced for each azimuth-range pixel, enabling the threshold shrinkage to be not only layer-varied but also element-wise. Moreover, the final learned thresholds can be visualized and combined with the SAR image semantics for mutual feedback. Finally, extensive experiments on simulated and real data are carried out to demonstrate the effectiveness and efficiency of the proposed method.
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作为一种引起巨大关注的新兴技术,通过分析继电器表面上的漫反射来重建隐藏物体的非视线(NLOS)成像,具有广泛的应用前景,在自主驾驶,医学成像和医学成像领域防御。尽管信噪比低(SNR)和高不良效率的挑战,但近年来,NLOS成像已迅速发展。大多数当前的NLOS成像技术使用传统的物理模型,通过主动或被动照明构建成像模型,并使用重建算法来恢复隐藏场景。此外,NLOS成像的深度学习算法最近也得到了很多关注。本文介绍了常规和深度学习的NLOS成像技术的全面概述。此外,我们还调查了新的拟议的NLOS场景,并讨论了现有技术的挑战和前景。这样的调查可以帮助读者概述不同类型的NLOS成像,从而加速了在角落周围看到的发展。
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信号处理是几乎任何传感器系统的基本组件,具有不同科学学科的广泛应用。时间序列数据,图像和视频序列包括可以增强和分析信息提取和量化的代表性形式的信号。人工智能和机器学习的最近进步正在转向智能,数据驱动,信号处理的研究。该路线图呈现了最先进的方法和应用程序的关键概述,旨在突出未来的挑战和对下一代测量系统的研究机会。它涵盖了广泛的主题,从基础到工业研究,以简明的主题部分组织,反映了每个研究领域的当前和未来发展的趋势和影响。此外,它为研究人员和资助机构提供了识别新前景的指导。
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快速移动对象的检测和跟踪在许多领域都具有广泛的实用性。但是,由于复杂的计算和有限的数据处理能力,使用基于图像的技术满足快速有效检测和跟踪的这种需求是有问题的。为了解决这个问题,我们提出了一种无图像的方法,以实现快速移动对象的实时检测和跟踪。它采用Hadamard模式通过空间光调节器来照亮快速移动对象,其中单像素检测器收集所得的光信号。单像素测量值直接用于无需图像重建而无需重建位置信息。此外,一种新的采样方法用于优化实现超低采样率的模式投影方法。与最先进的方法相比,我们的方法不仅能够处理实时检测和跟踪,而且还具有少量计算和高效率。我们在实验上证明,使用22kHz数字微型摩尔设备的提出方法可以在跟踪时以1.28%的采样速率实现105FPS帧速率。我们的方法突破了传统的跟踪方式,可以在无图像重建的情况下实现对象实时跟踪。
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As a common weather, rain streaks adversely degrade the image quality. Hence, removing rains from an image has become an important issue in the field. To handle such an ill-posed single image deraining task, in this paper, we specifically build a novel deep architecture, called rain convolutional dictionary network (RCDNet), which embeds the intrinsic priors of rain streaks and has clear interpretability. In specific, we first establish a RCD model for representing rain streaks and utilize the proximal gradient descent technique to design an iterative algorithm only containing simple operators for solving the model. By unfolding it, we then build the RCDNet in which every network module has clear physical meanings and corresponds to each operation involved in the algorithm. This good interpretability greatly facilitates an easy visualization and analysis on what happens inside the network and why it works well in inference process. Moreover, taking into account the domain gap issue in real scenarios, we further design a novel dynamic RCDNet, where the rain kernels can be dynamically inferred corresponding to input rainy images and then help shrink the space for rain layer estimation with few rain maps so as to ensure a fine generalization performance in the inconsistent scenarios of rain types between training and testing data. By end-to-end training such an interpretable network, all involved rain kernels and proximal operators can be automatically extracted, faithfully characterizing the features of both rain and clean background layers, and thus naturally lead to better deraining performance. Comprehensive experiments substantiate the superiority of our method, especially on its well generality to diverse testing scenarios and good interpretability for all its modules. Code is available in \emph{\url{https://github.com/hongwang01/DRCDNet}}.
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低成本毫米波(MMWAVE)通信和雷达设备的商业可用性开始提高消费市场中这种技术的渗透,为第五代(5G)的大规模和致密的部署铺平了道路(5G) - 而且以及6G网络。同时,普遍存在MMWAVE访问将使设备定位和无设备的感测,以前所未有的精度,特别是对于Sub-6 GHz商业级设备。本文使用MMWAVE通信和雷达设备在基于设备的定位和无设备感应中进行了现有技术的调查,重点是室内部署。我们首先概述关于MMWAVE信号传播和系统设计的关键概念。然后,我们提供了MMWaves启用的本地化和感应方法和算法的详细说明。我们考虑了在我们的分析中的几个方面,包括每个工作的主要目标,技术和性能,每个研究是否达到了一定程度的实现,并且该硬件平台用于此目的。我们通过讨论消费者级设备的更好算法,密集部署的数据融合方法以及机器学习方法的受过教育应用是有前途,相关和及时的研究方向的结论。
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自动化驾驶系统(广告)开辟了汽车行业的新领域,为未来的运输提供了更高的效率和舒适体验的新可能性。然而,在恶劣天气条件下的自主驾驶已经存在,使自动车辆(AVS)长时间保持自主车辆(AVS)或更高的自主权。本文评估了天气在分析和统计方式中为广告传感器带来的影响和挑战,并对恶劣天气条件进行了解决方案。彻底报道了关于对每种天气的感知增强的最先进技术。外部辅助解决方案如V2X技术,当前可用的数据集,模拟器和天气腔室的实验设施中的天气条件覆盖范围明显。通过指出各种主要天气问题,自主驾驶场目前正在面临,近年来审查硬件和计算机科学解决方案,这项调查概述了在不利的天气驾驶条件方面的障碍和方向的障碍和方向。
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电磁(EM)成像广泛用于感应安全性,生物医学,地球物理学和各种行业。这是一个不当的逆问题,其解决方案通常在计算上昂贵。机器学习(ML)技术,尤其是深度学习(DL)在快速准确的成像中显示出潜力。但是,纯粹的数据驱动方法的高性能依赖于构建与实用方案一致的训练集,而在EM成像任务中通常不可能。因此,普遍性成为主要问题。另一方面,物理原理是EM现象的基础,并为当前的成像技术提供了基准。为了从大数据中的先验知识和物理定律的理论约束中受益,物理学嵌入的ML成像方法已成为近期大量工作的重点。本文调查了各种方案,以将物理学纳入基于学习的EM成像中。我们首先介绍有关逆问题的EM成像和基本公式的背景。然后,我们专注于将物理和ML进行线性和非线性成像组合的三种类型的策略,并讨论它们的优势和局限性。最后,我们在这个快速发展的领域中以公开的挑战和可能的前进方式得出结论。我们的目的是促进将有效,可解释和可控制的智能EM成像方法的研究。
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可以使用X射线自由电子激光器的强脉冲和短脉冲直接通过单次相干衍射成像直接观察到自由飞行中孤立的纳米样品的结构和动力学。广角散射图像甚至编码样品的三维形态信息,但是该信息的检索仍然是一个挑战。到目前为止,只有通过与高度约束模型拟合,需要对单镜头实现有效的三维形态重建,这需要有关可能的几何形状的先验知识。在这里,我们提出了一种更通用的成像方法。依赖于允许凸多面体描述的任何样品形态的模型,我们从单个银纳米颗粒中重建广角衍射模式。除了具有高对称性的已知结构动机外,我们还检索了以前无法访问的不完美形状和聚集物。我们的结果为单个纳米颗粒的真实3D结构确定以及最终的超快纳米级动力学的3D电影开辟了新的途径。
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随着Terahertz(THZ)信号产生和辐射方法的最新进展,关节通信和传感应用正在塑造无线系统的未来。为此,预计将在用户设备设备上携带THZ光谱,以识别感兴趣的材料和气态组件。 THZ特异性的信号处理技术应补充这种对THZ感应的重新兴趣,以有效利用THZ频带。在本文中,我们介绍了这些技术的概述,重点是信号预处理(标准的正常差异归一化,最小值 - 最大归一化和Savitzky-Golay滤波),功能提取(主成分分析,部分最小二乘,t,T,T部分,t部分,t部分正方形,T - 分布的随机邻居嵌入和非负矩阵分解)和分类技术(支持向量机器,k-nearest邻居,判别分析和天真的贝叶斯)。我们还通过探索他们在THZ频段的有希望的传感能力来解决深度学习技术的有效性。最后,我们研究了在联合通信和传感的背景下,研究方法的性能和复杂性权衡;我们激励相应的用例,并在该领域提供未来的研究方向。
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Deep learning (DL)-based tomographic SAR imaging algorithms are gradually being studied. Typically, they use an unfolding network to mimic the iterative calculation of the classical compressive sensing (CS)-based methods and process each range-azimuth unit individually. However, only one-dimensional features are effectively utilized in this way. The correlation between adjacent resolution units is ignored directly. To address that, we propose a new model-data-driven network to achieve tomoSAR imaging based on multi-dimensional features. Guided by the deep unfolding methodology, a two-dimensional deep unfolding imaging network is constructed. On the basis of it, we add two 2D processing modules, both convolutional encoder-decoder structures, to enhance multi-dimensional features of the imaging scene effectively. Meanwhile, to train the proposed multifeature-based imaging network, we construct a tomoSAR simulation dataset consisting entirely of simulation data of buildings. Experiments verify the effectiveness of the model. Compared with the conventional CS-based FISTA method and DL-based gamma-Net method, the result of our proposed method has better performance on completeness while having decent imaging accuracy.
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Deep neural networks provide unprecedented performance gains in many real world problems in signal and image processing. Despite these gains, future development and practical deployment of deep networks is hindered by their blackbox nature, i.e., lack of interpretability, and by the need for very large training sets. An emerging technique called algorithm unrolling or unfolding offers promise in eliminating these issues by providing a concrete and systematic connection between iterative algorithms that are used widely in signal processing and deep neural networks. Unrolling methods were first proposed to develop fast neural network approximations for sparse coding. More recently, this direction has attracted enormous attention and is rapidly growing both in theoretic investigations and practical applications. The growing popularity of unrolled deep networks is due in part to their potential in developing efficient, high-performance and yet interpretable network architectures from reasonable size training sets. In this article, we review algorithm unrolling for signal and image processing. We extensively cover popular techniques for algorithm unrolling in various domains of signal and image processing including imaging, vision and recognition, and speech processing. By reviewing previous works, we reveal the connections between iterative algorithms and neural networks and present recent theoretical results. Finally, we provide a discussion on current limitations of unrolling and suggest possible future research directions.
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求解电磁逆散射问题(ISP)由于内在的非线性,呈不良和昂贵的计算成本,挑战。最近,深神经网络(DNN)技术已经成功地应用于ISP上,并在传统方法上示出了优异成像的电位。在本文中,我们分析了DNN溶剂和传统迭代算法之间的类比,并讨论了在训练过程中不能有效地纳入重要的物理现象。我们展示了在DNN的学习过程中包括近端前瞻的重要性。为此,我们提出了新的损耗功能设计,其包括基于多散射的近场数量(例如散射场或感兴趣领域内的诱导电流)。使用各种数值实验研究了物理引导功能的影响。总结了调查的ISP求解器的利弊,综述了不同损失功能。
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本文介绍了在混合高斯 - 突破噪声条件下重建高分辨率(HR)LF图像的GPU加速计算框架。主要重点是考虑处理速度和重建质量的高性能方法。从统计的角度来看,我们得出了一个联合$ \ ell^1 $ - $ \ ell^2 $数据保真度,用于惩罚人力资源重建错误,考虑到混合噪声情况。对于正则化,我们采用了加权非本地总变异方法,这使我们能够通过适当的加权方案有效地实现LF图像。我们表明,乘数算法(ADMM)的交替方向方法可用于简化计算复杂性,并在GPU平台上导致高性能并行计算。对合成4D LF数据集和自然图像数据集进行了广泛的实验,以验证提出的SR模型的鲁棒性并评估加速优化器的性能。实验结果表明,与最先进的方法相比,我们的方法在严重的混合噪声条件下实现了更好的重建质量。此外,提议的方法克服了处理大规模SR任务的先前工作的局限性。虽然适合单个现成的GPU,但建议的加速器提供的平均加速度为2.46 $ \ times $和1.57 $ \ times $,分别为$ \ times 2 $和$ \ times 3 $ SR任务。此外,与CPU执行相比,达到$ 77 \ times $的加速。
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从X射线冠状动脉造影(XCA)图像序列中提取对比度的血管对于直觉诊断和治疗具有重要的临床意义。在这项研究中,XCA图像序列O被认为是三维张量输入,血管层H是稀疏张量,而背景层B是低级别张量。使用张量核标准(TNN)最小化,提出了一种基于张量的强稳定主成分分析(TRPCA)的新型血管层提取方法。此外,考虑了血管的不规则运动和周围无关组织的动态干扰,引入了总变化(TV)正规化时空约束,以分离动态背景E。 - 阶段区域生长(TSRG)方法用于血管增强和分割。全局阈值分割用作获得主分支的预处理,并使用ra样特征(RLF)滤波器来增强和连接破碎的小段,最终的容器掩模是通过结合两个中间结果来构建的。我们评估了TV-TRPCA算法的前景提取的可见性以及TSRG算法在真实临床XCA图像序列和第三方数据库上的血管分割的准确性。定性和定量结果都验证了所提出的方法比现有的最新方法的优越性。
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检测有害的携带物体在智能监控系统中起着关键作用,例如,在机场安全中具有广泛的应用。在本文中,我们专注于使用低成本77GHz MMWVEAVE雷达的相对未开发的区域,用于携带物体检测问题。该建议的系统能够实时检测三类对象 - 笔记本电脑,手机和刀具 - 在开放的携带和隐藏的情况下,物体隐藏着衣服或袋子。这种能力是通过用于定位的初始信号处理来实现的,用于定位和生成范围 - 方位角升降图像立方体,然后是基于深度学习的预测网络和用于检测对象的多枪后处理模块。用于验证检测开放携带和隐藏物体的系统性能的广泛实验已经提出了一种自制雷达相机测试用和数据集。此外,分析了不同输入,因素和参数对系统性能的影响,为系统提供了直观的理解。该系统是旨在使用77GHz雷达检测携带物体的其他未来作品的第一个基线。
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兴趣点检测是计算机视觉和图像处理中最根本,最关键的问题之一。在本文中,我们对图像特征信息(IFI)提取技术进行了全面综述,以进行利益点检测。为了系统地介绍现有的兴趣点检测方法如何从输入图像中提取IFI,我们提出了IFI提取技术的分类学检测。根据该分类法,我们讨论了不同类型的IFI提取技术以进行兴趣点检测。此外,我们确定了与现有的IFI提取技术有关的主要未解决的问题,以及以前尚未讨论过的任何兴趣点检测方法。提供了现有的流行数据集和评估标准,并评估和讨论了18种最先进方法的性能。此外,还详细阐述了有关IFI提取技术的未来研究方向。
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社会偏移和温度筛选已被广泛用于抵消Covid-19大流行,从全世界的学术界,工业和公共主管部门引发极大的兴趣。虽然大多数解决方案分别处理了这些方面,但它们的组合将极大地利用对公共空间的持续监测,并有助于触发有效的对策。这项工作介绍了毫米杀虫雷达和红外成像传感系统,在室内空间中进行了不引人注目的和隐私,在室内空间中进行了不显眼和隐私。 Millitrace-IR通过强大的传感器融合方法,MM波雷达和红外热摄像机结合。它通过在热摄像机图像平面和雷达参考系统中的人体运动中共同跟踪受试者的面,实现了偏移和体温的完全自动测量。此外,毫米itrace-IR执行接触跟踪:热相机传感器可靠地检测体温高的人,随后通过雷达以非侵入方式追踪大型室内区域。进入新房间时,通过深神经网络从雷达反射计算与雷达反射的步态相关的特征,并使用加权的极端学习机作为最终重新识别工具,在其他人之间重新识别一个主题。从实际实施中获得的实验结果,从毫米 - IR的实际实施中展示了距离/轨迹估计的排入量级精度,个人间距离估计(对受试者接近0.2米的受试者有效),以及精确的温度监测(最大误差0.5 {\ deg} c)。此外,毫米itrace-IR通过高精度(95%)的人重新识别,在不到20秒内提供接触跟踪。
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