This paper explores the recovery of block sparse signals in frame-based settings using the l_(2)/l_(q)-synthesis technique(0<q≤1).We propose a new null space property,referred to as block D-NSP_(q),which is based ...This paper explores the recovery of block sparse signals in frame-based settings using the l_(2)/l_(q)-synthesis technique(0<q≤1).We propose a new null space property,referred to as block D-NSP_(q),which is based on the dictionary D.We establish that matrices adhering to the block D-NSP_(q)condition are both necessary and sufficient for the exact recovery of block sparse signals via l_(2)/l_(q)-synthesis.Additionally,this condition is essential for the stable recovery of signals that are block-compressible with respect to D.This D-NSP_(q)property is identified as the first complete condition for successful signal recovery using l_(2)/l_(q)-synthesis.Furthermore,we assess the theoretical efficacy of the l2/lq-synthesis method under conditions of measurement noise.展开更多
A joint two-dimensional(2D)direction-of-arrival(DOA)and radial Doppler frequency estimation method for the L-shaped array is proposed in this paper based on the compressive sensing(CS)framework.Revised from the conven...A joint two-dimensional(2D)direction-of-arrival(DOA)and radial Doppler frequency estimation method for the L-shaped array is proposed in this paper based on the compressive sensing(CS)framework.Revised from the conventional CS-based methods where the joint spatial-temporal parameters are characterized in one large scale matrix,three smaller scale matrices with independent azimuth,elevation and Doppler frequency are introduced adopting a separable observation model.Afterwards,the estimation is achieved by L1-norm minimization and the Bayesian CS algorithm.In addition,under the L-shaped array topology,the azimuth and elevation are separated yet coupled to the same radial Doppler frequency.Hence,the pair matching problem is solved with the aid of the radial Doppler frequency.Finally,numerical simulations corroborate the feasibility and validity of the proposed algorithm.展开更多
Media based modulation(MBM)is expected to be a prominent modulation scheme,which has access to the high data rate by using radio frequency(RF)mirrors and fewer transmit antennas.Associated with multiuser multiple inpu...Media based modulation(MBM)is expected to be a prominent modulation scheme,which has access to the high data rate by using radio frequency(RF)mirrors and fewer transmit antennas.Associated with multiuser multiple input multiple output(MIMO),the MBM scheme achieves better performance than other conventional multiuser MIMO schemes.In this paper,the massive MIMO uplink is considered and a conjunctive MBM transmission scheme for each user is employed.This conjunctive MBM transmission scheme gathers aggregate MBM signals in multiple continuous time slots,which exploits the structured sparsity of these aggregate MBM signals.Under this kind of scenario,a multiuser detector with low complexity based on the compressive sensing(CS)theory to gain better detection performance is proposed.This detector is developed from the greedy sparse recovery technique compressive sampling matching pursuit(CoSaMP)and exploits not only the inherently distributed sparsity of MBM signals but also the structured sparsity of multiple aggregate MBM signals.By exploiting these sparsity,the proposed CoSaMP based multiuser detector achieves reliable detection with low complexity.Simulation results demonstrate that the proposed CoSaMP based multiuser detector achieves better detection performance compared with the conventional methods.展开更多
It is understood that the sparse signal recovery with a standard compressive sensing（CS） strategy requires the measurement matrix known as a priori. The measurement matrix is, however, often perturbed in a practical...It is understood that the sparse signal recovery with a standard compressive sensing（CS） strategy requires the measurement matrix known as a priori. The measurement matrix is, however, often perturbed in a practical application.In order to handle such a case, an optimization problem by exploiting the sparsity characteristics of both the perturbations and signals is formulated. An algorithm named as the sparse perturbation signal recovery algorithm（SPSRA） is then proposed to solve the formulated optimization problem. The analytical results show that our SPSRA can simultaneously recover the signal and perturbation vectors by an alternative iteration way, while the convergence of the SPSRA is also analytically given and guaranteed. Moreover, the support patterns of the sparse signal and structured perturbation shown are the same and can be exploited to improve the estimation accuracy and reduce the computation complexity of the algorithm. The numerical simulation results verify the effectiveness of analytical ones.展开更多
目的:探讨三维可变翻转角快速自旋回波(sampling perfection with application optimized contrast using different flip angle evolution,SPACE)序列联合压缩感知(compressed sensing,CS)技术在肩锁关节损伤诊断中的应用价值。方法:...目的:探讨三维可变翻转角快速自旋回波(sampling perfection with application optimized contrast using different flip angle evolution,SPACE)序列联合压缩感知(compressed sensing,CS)技术在肩锁关节损伤诊断中的应用价值。方法:前瞻性地纳入2023年5月—2024年2月在南京医科大学第一附属医院就诊的有肩部外伤史、临床怀疑肩锁关节损伤的患者34例,对患者分别进行常规二维(two-dimension,2D)磁共振序列和基于CS的3D CS-SPACE序列扫描。分别在两组图像上测量肱二头肌长头腱和肱骨骨髓腔的信号强度和标准差,并计算出信噪比(signal to noise ratio,SNR)、对比噪声比(contrast to noise ratio,CNR);3位医生分别通过两组图像评估肩锁关节的损伤情况并给出其诊断信心评级。比较两组图像骨髓腔、肱二头肌长头腱的SNR、CNR以及诊断信心评级;分别分析3位医生在常规2D图像中的诊断一致性和3D CS-SPACE图像中的诊断一致性,最后评估两组图像之间的诊断一致性。结果:图像质量的客观评价中,3D CS-SPACE图像的SNR和CNR均明显优于常规2D图像;两组图像诊断信心的评级,2位医生的3D CS-SPACE图像评级明显高于常规2D图像,1位医生评级差异无统计学意义;3位医生在常规2D图像和3D CS-SPACE图像上对肩锁关节损伤的评估均具有较高的一致性(κ均>0.6),两组图像对肩锁关节的损伤评估具有较高的一致性(κ均>0.6)。结论:对于肩锁关节损伤的诊断,3D CS-SPACE图像与常规2D图像具有较高的一致性,且3D CS-SPACE序列能够在缩短扫描时间的同时获得更好的图像质量。展开更多
In order to apply compressive sensing in wireless sensor network, inside the nodes cluster classified by the spatial correlation, we propose that a cluster head adopts free space optical communication with space divis...In order to apply compressive sensing in wireless sensor network, inside the nodes cluster classified by the spatial correlation, we propose that a cluster head adopts free space optical communication with space division multiple access, and a sensor node uses a modulating retro-reflector for communication. Thus while a random sampling matrix is used to guide the establishment of links between head cluster and sensor nodes, the random linear projection is accomplished. To establish multiple links at the same time, an optical space division multiple access antenna is designed. It works in fixed beams switching mode and consists of optic lens with a large field of view（FOV）, fiber array on the focal plane which is used to realize virtual channels segmentation, direction of arrival sensor, optical matrix switch and controller. Based on the angles of nodes＇ laser beams, by dynamically changing the route, optical matrix switch actualizes the multi-beam full duplex tracking receiving and transmission. Due to the structure of fiber array, there will be several fade zones both in the focal plane and in lens＇ FOV. In order to lower the impact of fade zones and harmonize multibeam, a fiber array adjustment is designed. By theoretical, simulated and experimental study, the antenna＇s qualitative feasibility is validated.展开更多
This paper extends the application of compressive sensing(CS) to the radar reconnaissance receiver for receiving the multi-narrowband signal. By combining the concept of the block sparsity, the self-adaption methods, ...This paper extends the application of compressive sensing(CS) to the radar reconnaissance receiver for receiving the multi-narrowband signal. By combining the concept of the block sparsity, the self-adaption methods, the binary tree search,and the residual monitoring mechanism, two adaptive block greedy algorithms are proposed to achieve a high probability adaptive reconstruction. The use of the block sparsity can greatly improve the efficiency of the support selection and reduce the lower boundary of the sub-sampling rate. Furthermore, the addition of binary tree search and monitoring mechanism with two different supports self-adaption methods overcome the instability caused by the fixed block length while optimizing the recovery of the unknown signal.The simulations and analysis of the adaptive reconstruction ability and theoretical computational complexity are given. Also, we verify the feasibility and effectiveness of the two algorithms by the experiments of receiving multi-narrowband signals on an analogto-information converter(AIC). Finally, an optimum reconstruction characteristic of two algorithms is found to facilitate efficient reception in practical applications.展开更多
The compressive sensing （CS） theory allows people to obtain signal in the frequency much lower than the requested one of sampling theorem. Because the theory is based on the assumption of that the location of sparse...The compressive sensing （CS） theory allows people to obtain signal in the frequency much lower than the requested one of sampling theorem. Because the theory is based on the assumption of that the location of sparse values is unknown, it has many constraints in practical applications. In fact, in many cases such as image processing, the location of sparse values is knowable, and CS can degrade to a linear process. In order to take full advantage of the visual information of images, this paper proposes the concept of dimensionality reduction transform matrix and then se- lects sparse values by constructing an accuracy control matrix, so on this basis, a degradation algorithm is designed that the signal can be obtained by the measurements as many as sparse values and reconstructed through a linear process. In comparison with similar methods, the degradation algorithm is effective in reducing the number of sensors and improving operational efficiency. The algorithm is also used to achieve the CS process with the same amount of data as joint photographic exports group （JPEG） compression and acquires the same display effect.展开更多
It is known that detecting small moving objects in as- tronomical image sequences is a significant research problem in space surveillance. The new theory, compressive sensing, pro- vides a very easy and computationall...It is known that detecting small moving objects in as- tronomical image sequences is a significant research problem in space surveillance. The new theory, compressive sensing, pro- vides a very easy and computationally cheap coding scheme for onboard astronomical remote sensing. An algorithm for small moving space object detection and localization is proposed. The algorithm determines the measurements of objects by comparing the difference between the measurements of the current image and the measurements of the background scene. In contrast to reconstruct the whole image, only a foreground image is recon- structed, which will lead to an effective computational performance, and a high level of localization accuracy is achieved. Experiments and analysis are provided to show the performance of the pro- posed approach on detection and localization.展开更多
The encoding aperture snapshot spectral imaging system,based on the compressive sensing theory,can be regarded as an encoder,which can efficiently obtain compressed two-dimensional spectral data and then decode it int...The encoding aperture snapshot spectral imaging system,based on the compressive sensing theory,can be regarded as an encoder,which can efficiently obtain compressed two-dimensional spectral data and then decode it into three-dimensional spectral data through deep neural networks.However,training the deep neural net⁃works requires a large amount of clean data that is difficult to obtain.To address the problem of insufficient training data for deep neural networks,a self-supervised hyperspectral denoising neural network based on neighbor⁃hood sampling is proposed.This network is integrated into a deep plug-and-play framework to achieve self-supervised spectral reconstruction.The study also examines the impact of different noise degradation models on the fi⁃nal reconstruction quality.Experimental results demonstrate that the self-supervised learning method enhances the average peak signal-to-noise ratio by 1.18 dB and improves the structural similarity by 0.009 compared with the supervised learning method.Additionally,it achieves better visual reconstruction results.展开更多
目的探讨压缩感知结合层面编码金属伪影校正(compressed sensing-slice-encoding metal artifact correction,CS-SEMAC)技术用于脊柱金属植入物术后MRI的应用价值。材料与方法比较招募的35例脊柱金属植入物术后患者3.0 T MRI矢状位CS-SE...目的探讨压缩感知结合层面编码金属伪影校正(compressed sensing-slice-encoding metal artifact correction,CS-SEMAC)技术用于脊柱金属植入物术后MRI的应用价值。材料与方法比较招募的35例脊柱金属植入物术后患者3.0 T MRI矢状位CS-SEMAC序列、高带宽(high bandwidth,HBW)序列和水脂分离(Dixon)三种序列在金属植入物伪影面积、椎体信噪比(signal-to-noise ratio,SNR)、图像质量、图像清晰度、脂肪抑制效果以及植入物周围解剖结构的可见性方面的差异。结果CS-SEMAC在T1、T2矢状位图像上金属伪影面积分别为(15.45±6.84)、(22.23±9.76)cm^(2),显著低于其他两种序列,差异具有统计学意义(P<0.001);三种序列在T2抑脂矢状面图像上的SNR两两比较显示:HBW序列椎体SNR显著高于其他两种序列,Dixon序列椎体SNR显著低于其他两种序列,CS-SEMAC序列椎体SNR低于HBW序列,高于Dixon序列,差异均有统计学意义(P<0.001);在图像清晰度上,T2WI-tirm-CS-SEMAC序列评分低于其他两种序列,差异具有统计学意义(P<0.001);T2WI-tirm-CS-SEMAC序列在图像质量和脂肪抑制效果方面评分显著优于其他两种序列,差异具有统计学意义(P<0.001);并且CS-SEMAC序列相较于其他两种序列更能清晰显示植入物周围椎体、椎弓根、椎间孔及神经根,差异具有统计学意义(P<0.001)。结论CS-SEMAC序列相比于HBW、Dixon序列能够有效减少植入物周围的金属伪影,并且能显著提高T2抑脂序列的图像质量和脂肪抑制效果,虽然在T2抑脂上金属植入物邻近椎体SNR相比HBW序列有所下降,图像比HBW和Dixon图像略模糊,但是椎体周围关键解剖结构的可见度明显提升,对脊柱术后解剖结构的显示有一定优势。展开更多
太赫兹成像技术虽已被证实能够用于检测葵花籽内部品质,然而其成像速度较为缓慢,难以实现切实且迅速的检测。为了实现对葵花籽饱满度的快速检测,该研究将压缩感知与注意力增强超分辨率生成对抗网络(A-ESRGAN)模型相结合应用于太赫兹成...太赫兹成像技术虽已被证实能够用于检测葵花籽内部品质,然而其成像速度较为缓慢,难以实现切实且迅速的检测。为了实现对葵花籽饱满度的快速检测,该研究将压缩感知与注意力增强超分辨率生成对抗网络(A-ESRGAN)模型相结合应用于太赫兹成像领域。首先,选用压缩采样匹配追踪(compressive sampling matching pursuit,CoSaMP)重构算法来验证不同测量矩阵的性能,根据最佳综合性能选取高斯矩阵作为测量矩阵。其次,通过比较基于交替方向乘子法(alternating direction method of multipliers,ADMM)结合全变分(total variation,TV)正则化(ADMM_TV)和子空间追踪(subspace pursuit,SP)等5种重构算法的峰值信噪比和重构时间等评价指标评估图像重建质量。结果表明ADMM_TV在峰值信噪比、均方误差、结构相似性指数表现最佳,自然图像质量评估器在测量比例超过6.0%最低,尽管重构时间无明显优势,但综合表现优于其他算法。最后,运用多尺度注意力增强超分辨率生成对抗网络(A-ESRGANmulti)模型对压缩感知不同采样率的重构图像进行处理,其效果优于真实图像增强超分辨率生成对抗网络(RealESRGAN)和单尺度注意力增强超分辨率生成对抗网络(A-ESRGAN-single),提升了图像质量,使边缘对比度得以提高,为后续的图像分割提供了便利。研究表明,压缩感知与A-ESRGAN-multi模型相结合用于检测葵花籽饱满度是可行的,验证集的饱满度误差平均为2.50%,最大检测误差为6.41%。综上所述,将压缩感知与A-ESRGAN-multi模型相结合,能够有效地节省82.5%的采样时间,为葵花籽的品质检测开辟了新的途径。展开更多
基金Supported by The Featured Innovation Projects of the General University of Guangdong Province(2023KTSCX096)The Special Projects in Key Areas of Guangdong Province(ZDZX1088)Research Team Project of Guangdong University of Education(2024KYCXTD018)。
文摘This paper explores the recovery of block sparse signals in frame-based settings using the l_(2)/l_(q)-synthesis technique(0<q≤1).We propose a new null space property,referred to as block D-NSP_(q),which is based on the dictionary D.We establish that matrices adhering to the block D-NSP_(q)condition are both necessary and sufficient for the exact recovery of block sparse signals via l_(2)/l_(q)-synthesis.Additionally,this condition is essential for the stable recovery of signals that are block-compressible with respect to D.This D-NSP_(q)property is identified as the first complete condition for successful signal recovery using l_(2)/l_(q)-synthesis.Furthermore,we assess the theoretical efficacy of the l2/lq-synthesis method under conditions of measurement noise.
文摘A joint two-dimensional(2D)direction-of-arrival(DOA)and radial Doppler frequency estimation method for the L-shaped array is proposed in this paper based on the compressive sensing(CS)framework.Revised from the conventional CS-based methods where the joint spatial-temporal parameters are characterized in one large scale matrix,three smaller scale matrices with independent azimuth,elevation and Doppler frequency are introduced adopting a separable observation model.Afterwards,the estimation is achieved by L1-norm minimization and the Bayesian CS algorithm.In addition,under the L-shaped array topology,the azimuth and elevation are separated yet coupled to the same radial Doppler frequency.Hence,the pair matching problem is solved with the aid of the radial Doppler frequency.Finally,numerical simulations corroborate the feasibility and validity of the proposed algorithm.
文摘Media based modulation(MBM)is expected to be a prominent modulation scheme,which has access to the high data rate by using radio frequency(RF)mirrors and fewer transmit antennas.Associated with multiuser multiple input multiple output(MIMO),the MBM scheme achieves better performance than other conventional multiuser MIMO schemes.In this paper,the massive MIMO uplink is considered and a conjunctive MBM transmission scheme for each user is employed.This conjunctive MBM transmission scheme gathers aggregate MBM signals in multiple continuous time slots,which exploits the structured sparsity of these aggregate MBM signals.Under this kind of scenario,a multiuser detector with low complexity based on the compressive sensing(CS)theory to gain better detection performance is proposed.This detector is developed from the greedy sparse recovery technique compressive sampling matching pursuit(CoSaMP)and exploits not only the inherently distributed sparsity of MBM signals but also the structured sparsity of multiple aggregate MBM signals.By exploiting these sparsity,the proposed CoSaMP based multiuser detector achieves reliable detection with low complexity.Simulation results demonstrate that the proposed CoSaMP based multiuser detector achieves better detection performance compared with the conventional methods.
基金supported by the National Natural Science Foundation of China(6107116361071164+5 种基金6147119161501233)the Fundamental Research Funds for the Central Universities(NP2014504)the Aeronautical Science Foundation(20152052026)the Electronic & Information School of Yangtze University Innovation Foundation(2016-DXCX-05)the Priority Academic Program Development of Jiangsu Higher Education Institutions
基金supported by the National Natural Science Foundation of China(61171127)
文摘It is understood that the sparse signal recovery with a standard compressive sensing（CS） strategy requires the measurement matrix known as a priori. The measurement matrix is, however, often perturbed in a practical application.In order to handle such a case, an optimization problem by exploiting the sparsity characteristics of both the perturbations and signals is formulated. An algorithm named as the sparse perturbation signal recovery algorithm（SPSRA） is then proposed to solve the formulated optimization problem. The analytical results show that our SPSRA can simultaneously recover the signal and perturbation vectors by an alternative iteration way, while the convergence of the SPSRA is also analytically given and guaranteed. Moreover, the support patterns of the sparse signal and structured perturbation shown are the same and can be exploited to improve the estimation accuracy and reduce the computation complexity of the algorithm. The numerical simulation results verify the effectiveness of analytical ones.
基金Supported by National Natural Science Foundation of China（61170147） Major Cooperation Project of Production and College in Fujian Province（2012H61010016） Natural Science Foundation of Fujian Province（2013J01234）
文摘目的:探讨三维可变翻转角快速自旋回波(sampling perfection with application optimized contrast using different flip angle evolution,SPACE)序列联合压缩感知(compressed sensing,CS)技术在肩锁关节损伤诊断中的应用价值。方法:前瞻性地纳入2023年5月—2024年2月在南京医科大学第一附属医院就诊的有肩部外伤史、临床怀疑肩锁关节损伤的患者34例,对患者分别进行常规二维(two-dimension,2D)磁共振序列和基于CS的3D CS-SPACE序列扫描。分别在两组图像上测量肱二头肌长头腱和肱骨骨髓腔的信号强度和标准差,并计算出信噪比(signal to noise ratio,SNR)、对比噪声比(contrast to noise ratio,CNR);3位医生分别通过两组图像评估肩锁关节的损伤情况并给出其诊断信心评级。比较两组图像骨髓腔、肱二头肌长头腱的SNR、CNR以及诊断信心评级;分别分析3位医生在常规2D图像中的诊断一致性和3D CS-SPACE图像中的诊断一致性,最后评估两组图像之间的诊断一致性。结果:图像质量的客观评价中,3D CS-SPACE图像的SNR和CNR均明显优于常规2D图像;两组图像诊断信心的评级,2位医生的3D CS-SPACE图像评级明显高于常规2D图像,1位医生评级差异无统计学意义;3位医生在常规2D图像和3D CS-SPACE图像上对肩锁关节损伤的评估均具有较高的一致性(κ均>0.6),两组图像对肩锁关节的损伤评估具有较高的一致性(κ均>0.6)。结论:对于肩锁关节损伤的诊断,3D CS-SPACE图像与常规2D图像具有较高的一致性,且3D CS-SPACE序列能够在缩短扫描时间的同时获得更好的图像质量。
基金supported by the National Natural Science Foundation of China(61372069)and the"111"Project(B08038)
文摘In order to apply compressive sensing in wireless sensor network, inside the nodes cluster classified by the spatial correlation, we propose that a cluster head adopts free space optical communication with space division multiple access, and a sensor node uses a modulating retro-reflector for communication. Thus while a random sampling matrix is used to guide the establishment of links between head cluster and sensor nodes, the random linear projection is accomplished. To establish multiple links at the same time, an optical space division multiple access antenna is designed. It works in fixed beams switching mode and consists of optic lens with a large field of view（FOV）, fiber array on the focal plane which is used to realize virtual channels segmentation, direction of arrival sensor, optical matrix switch and controller. Based on the angles of nodes＇ laser beams, by dynamically changing the route, optical matrix switch actualizes the multi-beam full duplex tracking receiving and transmission. Due to the structure of fiber array, there will be several fade zones both in the focal plane and in lens＇ FOV. In order to lower the impact of fade zones and harmonize multibeam, a fiber array adjustment is designed. By theoretical, simulated and experimental study, the antenna＇s qualitative feasibility is validated.
基金supported by the National Natural Science Foundation of China(61172159)
文摘This paper extends the application of compressive sensing(CS) to the radar reconnaissance receiver for receiving the multi-narrowband signal. By combining the concept of the block sparsity, the self-adaption methods, the binary tree search,and the residual monitoring mechanism, two adaptive block greedy algorithms are proposed to achieve a high probability adaptive reconstruction. The use of the block sparsity can greatly improve the efficiency of the support selection and reduce the lower boundary of the sub-sampling rate. Furthermore, the addition of binary tree search and monitoring mechanism with two different supports self-adaption methods overcome the instability caused by the fixed block length while optimizing the recovery of the unknown signal.The simulations and analysis of the adaptive reconstruction ability and theoretical computational complexity are given. Also, we verify the feasibility and effectiveness of the two algorithms by the experiments of receiving multi-narrowband signals on an analogto-information converter(AIC). Finally, an optimum reconstruction characteristic of two algorithms is found to facilitate efficient reception in practical applications.
基金supported by the National Natural Science Foundation of China (61077079)the Specialized Research Fund for the Doctoral Program of Higher Education (20102304110013)the Program Ex-cellent Academic Leaders of Harbin (2009RFXXG034)
文摘The compressive sensing （CS） theory allows people to obtain signal in the frequency much lower than the requested one of sampling theorem. Because the theory is based on the assumption of that the location of sparse values is unknown, it has many constraints in practical applications. In fact, in many cases such as image processing, the location of sparse values is knowable, and CS can degrade to a linear process. In order to take full advantage of the visual information of images, this paper proposes the concept of dimensionality reduction transform matrix and then se- lects sparse values by constructing an accuracy control matrix, so on this basis, a degradation algorithm is designed that the signal can be obtained by the measurements as many as sparse values and reconstructed through a linear process. In comparison with similar methods, the degradation algorithm is effective in reducing the number of sensors and improving operational efficiency. The algorithm is also used to achieve the CS process with the same amount of data as joint photographic exports group （JPEG） compression and acquires the same display effect.
基金supported by the National Natural Science Foundation of China (60903126)the China Postdoctoral Special Science Foundation (201003685)+1 种基金the China Postdoctoral Science Foundation (20090451397)the Northwestern Polytechnical University Foundation for Fundamental Research (JC201120)
文摘It is known that detecting small moving objects in as- tronomical image sequences is a significant research problem in space surveillance. The new theory, compressive sensing, pro- vides a very easy and computationally cheap coding scheme for onboard astronomical remote sensing. An algorithm for small moving space object detection and localization is proposed. The algorithm determines the measurements of objects by comparing the difference between the measurements of the current image and the measurements of the background scene. In contrast to reconstruct the whole image, only a foreground image is recon- structed, which will lead to an effective computational performance, and a high level of localization accuracy is achieved. Experiments and analysis are provided to show the performance of the pro- posed approach on detection and localization.
基金Supported by the Zhejiang Provincial"Jianbing"and"Lingyan"R&D Programs(2023C03012,2024C01126)。
文摘The encoding aperture snapshot spectral imaging system,based on the compressive sensing theory,can be regarded as an encoder,which can efficiently obtain compressed two-dimensional spectral data and then decode it into three-dimensional spectral data through deep neural networks.However,training the deep neural net⁃works requires a large amount of clean data that is difficult to obtain.To address the problem of insufficient training data for deep neural networks,a self-supervised hyperspectral denoising neural network based on neighbor⁃hood sampling is proposed.This network is integrated into a deep plug-and-play framework to achieve self-supervised spectral reconstruction.The study also examines the impact of different noise degradation models on the fi⁃nal reconstruction quality.Experimental results demonstrate that the self-supervised learning method enhances the average peak signal-to-noise ratio by 1.18 dB and improves the structural similarity by 0.009 compared with the supervised learning method.Additionally,it achieves better visual reconstruction results.
文摘目的探讨压缩感知结合层面编码金属伪影校正(compressed sensing-slice-encoding metal artifact correction,CS-SEMAC)技术用于脊柱金属植入物术后MRI的应用价值。材料与方法比较招募的35例脊柱金属植入物术后患者3.0 T MRI矢状位CS-SEMAC序列、高带宽(high bandwidth,HBW)序列和水脂分离(Dixon)三种序列在金属植入物伪影面积、椎体信噪比(signal-to-noise ratio,SNR)、图像质量、图像清晰度、脂肪抑制效果以及植入物周围解剖结构的可见性方面的差异。结果CS-SEMAC在T1、T2矢状位图像上金属伪影面积分别为(15.45±6.84)、(22.23±9.76)cm^(2),显著低于其他两种序列,差异具有统计学意义(P<0.001);三种序列在T2抑脂矢状面图像上的SNR两两比较显示:HBW序列椎体SNR显著高于其他两种序列,Dixon序列椎体SNR显著低于其他两种序列,CS-SEMAC序列椎体SNR低于HBW序列,高于Dixon序列,差异均有统计学意义(P<0.001);在图像清晰度上,T2WI-tirm-CS-SEMAC序列评分低于其他两种序列,差异具有统计学意义(P<0.001);T2WI-tirm-CS-SEMAC序列在图像质量和脂肪抑制效果方面评分显著优于其他两种序列,差异具有统计学意义(P<0.001);并且CS-SEMAC序列相较于其他两种序列更能清晰显示植入物周围椎体、椎弓根、椎间孔及神经根,差异具有统计学意义(P<0.001)。结论CS-SEMAC序列相比于HBW、Dixon序列能够有效减少植入物周围的金属伪影,并且能显著提高T2抑脂序列的图像质量和脂肪抑制效果,虽然在T2抑脂上金属植入物邻近椎体SNR相比HBW序列有所下降,图像比HBW和Dixon图像略模糊,但是椎体周围关键解剖结构的可见度明显提升,对脊柱术后解剖结构的显示有一定优势。
文摘太赫兹成像技术虽已被证实能够用于检测葵花籽内部品质,然而其成像速度较为缓慢,难以实现切实且迅速的检测。为了实现对葵花籽饱满度的快速检测,该研究将压缩感知与注意力增强超分辨率生成对抗网络(A-ESRGAN)模型相结合应用于太赫兹成像领域。首先,选用压缩采样匹配追踪(compressive sampling matching pursuit,CoSaMP)重构算法来验证不同测量矩阵的性能,根据最佳综合性能选取高斯矩阵作为测量矩阵。其次,通过比较基于交替方向乘子法(alternating direction method of multipliers,ADMM)结合全变分(total variation,TV)正则化(ADMM_TV)和子空间追踪(subspace pursuit,SP)等5种重构算法的峰值信噪比和重构时间等评价指标评估图像重建质量。结果表明ADMM_TV在峰值信噪比、均方误差、结构相似性指数表现最佳,自然图像质量评估器在测量比例超过6.0%最低,尽管重构时间无明显优势,但综合表现优于其他算法。最后,运用多尺度注意力增强超分辨率生成对抗网络(A-ESRGANmulti)模型对压缩感知不同采样率的重构图像进行处理,其效果优于真实图像增强超分辨率生成对抗网络(RealESRGAN)和单尺度注意力增强超分辨率生成对抗网络(A-ESRGAN-single),提升了图像质量,使边缘对比度得以提高,为后续的图像分割提供了便利。研究表明,压缩感知与A-ESRGAN-multi模型相结合用于检测葵花籽饱满度是可行的,验证集的饱满度误差平均为2.50%,最大检测误差为6.41%。综上所述,将压缩感知与A-ESRGAN-multi模型相结合,能够有效地节省82.5%的采样时间,为葵花籽的品质检测开辟了新的途径。
