合成孔径压缩感知超声成像中的高效能稀疏字典设计

针对目前常见的稀疏字典缺乏针对性,在合成孔径医学超声成像中的应用效果不佳,难以在低压缩率下保证重构图像质量的问题,本文设计了一种高效能的稀疏字典。根据超声回波信号是由发射脉冲信号经过不同延时衰减后叠加的特点,利用发射脉冲作为基函数构造稀疏字典,回波信号在该稀疏字典确定的变换域中具备很好的稀疏性,理论上能使其稀疏表示系数的稀疏度等于超声阵元接收到的反射回波数。通过FieldⅡ对简单点目标和复杂目标的仿真结果表明:在相同的重构算法和压缩率下该稀疏字典重构的平均绝对误差明显小于常见的稀疏字典,其值仅为DWT的几分之一,DFT和DCT的几十分之一,能让回波信号以更低的压缩率实现相同的恢复效果。本文最后使用体模的实际采集数据对算法的实际效果进行检测,实验结果也与仿真结果基本一致。基于该稀疏字典的压缩感知算法可以进一步减少合成孔径成像所需存储的数据量、降低系统的复杂度。      

合成孔径聚焦超声成像(三)

在合成孔径超声成像中，用小孔径基元换能器发射声波，并继而采集被目标或缺陷散射产生的回波信号．数据采集完成后，通过处理才能得到高分辨的重建声图．对种种不同的合成孔径成像技术而言，成像过程都可以分为两步：1．数据采集和存储；2．图像重建．在第一步中由基元换能器完成的电一声、声一电信号转换功能，及由后续电路完成的信号量化、采集功能，对各种不同的合成孔径成像技术而言，在原理上是没有多少不同的．各种不同的成像系统间的差别，主要在于所应用的图像重建原理及算法．在第一讲及第二讲中，我们分别介绍了基于等效线阵及匹…     

逆合成孔径成像激光雷达数据采样技术

逆合成孔径成像激光雷达能够实现对运动目标的高分辨实时成像,但激光信号的极大带宽和目标回波信号的微弱性给雷达回波数据的接收和处理带来了较大困难.针对这一问题,提出了基于光外差探测手段和压缩感知理论相结合的信号采样方法,首先通过光外差探测降低回波信号的有效带宽,再结合压缩感知理论实现对信号的稀疏化采样和重构.仿真结果证明了运用本文所提出的采样方法,在使用远低于奈奎斯特定理所规定的采样率时,仍然能够实现对目标的高质量成像.     

逆合成孔径成像激光雷达数据采样技术

逆合成孔径成像激光雷达能够实现对运动目标的高分辨实时成像,但激光信号的极大带宽和目标回波信号的微弱性给雷达回波数据的接收和处理带来了较大困难.针对这一问题,提出了基于光外差探测手段和压缩感知理论相结合的信号采样方法,首先通过光外差探测降低回波信号的有效带宽,再结合压缩感知理论实现对信号的稀疏化采样和重构.仿真结果证明了运用本文所提出的采样方法,在使用远低于奈奎斯特定理所规定的采样率时,仍然能够实现对目标的高质量成像.     

选择性双向顺序压缩感知重建动态磁共振成像

压缩感知是一种新兴技术,该技术能够用远低于奈奎斯特采样频率采集的信号恢复出原始信号. 压缩感知成像方法大大提高了心脏磁共振成像的采集速度,已有的方法主要利用动态图像时间相关及心脏的周期性运动特征,如采用在时间维做傅立叶变换或求解每帧数据跟参考帧数据的差异获取稀疏数据,满足压缩感知重建的要求. 该文提出了选择性双向顺序压缩感知重建算法,利用相邻帧的差异更小的特点,获取更加稀疏的差异数据,同时利用动态图像的周期性,以目标函数积分为判据,在时间顺序和时间逆序两个方向选择效果更好的方向进行数据重建,降低图像伪影和噪声. 该选择算法,可以在不增加重建时间的情况下,选择双向顺序重建中最佳的结果. 该文对心脏磁共振图像数据进行了数据处理实验,并且跟传统压缩感知算法、参考帧差异方法及匙孔成像方法进行了比较. 结果表明：该方法无论从视觉效果还是从统计结果上,都有很大的改善.     

压缩感知理论在光学成像中的应用

压缩感知以信号的稀疏性或可压缩性为条件,以远低于耐奎斯特采样频率对信号数据进行采样和编码。简要概括了压缩感知的基本理论,它采用非自适应线性投影来保持信号的原始结构, 能通过数值最优化问题精确或高概率地重构原始信号。详细介绍了其在光学成像系统中的应用,主要包括单像素相机、超薄成像、编码孔径成像、多路技术智能成像、多光谱成像和CMOS成像等成像系统。最后对该理论的应用前景进行了阐述。     

合成孔径激光成像雷达(Ⅲ):双向环路发射接收望远镜

提出了一种用于合成孔径激光成像雷达的双向环路结构的发射接收望远镜,双向环路包括发射4-f转像系统、接收4-f转像系统和独立的望远镜.发射通道中设置离焦和相位调制平板偏置,接收通道中设置离焦和相位平板偏置.控制发射离焦量,发射相位调制甬数.接收离焦量.接收相位调制甬数.用同一个望远镜可以同时实现空间二次项相位附加偏置的激光发射和消除目标点散射回波接收波面像差的离焦光学接收,并产生雷达运动方向上合适的和可控制的相位二次项历程.从而实现孔径合成成像.详细介绍了系统设计,给出了从发射到光电外差接收的全过程传输方程.     

低信噪比下的二维联合线性布雷格曼迭代快速超分辨成像算法

针对实际逆合成孔径雷达(ISAR)成像时带宽有限、方位孔径稀疏的小角度回波数据条件下,常规算法的成像分辨率不高等问题,基于压缩感知理论,提出了一种低信噪比条件下的二维联合布雷格曼迭代快速ISAR超分辨成像算法.首先,将雷达回波构建为距离频域-方位多普勒域的二维稀疏表示模型,在此基础上,将二维超分辨成像问题转换为二维联合压缩感知的稀疏重构问题;其次,为了避免重构时向量化操作带来的复杂度,提出了二维联合布雷格曼迭代算法,为实现快速重构,将加权残量迭代、估计停滞步长与感知矩阵条件数优化三种加快收敛速度的思想相结合,既利用了布雷格曼迭代在低信噪比条件下的重构能力又能保证快速成像.最后仿真实验结果表明在欠采样和低信噪比条件下本文算法能够缩短成像时间,且具备更好的噪声鲁棒性.     

时域压缩合成孔径超分辨水声成像算法

提出一种针对水下稀疏目标的时域压缩合成孔径声呐成像方法(TC-SAS),实现了水声目标高分辨实时成像。通过多子阵的孔径合成,在时域上构造出成像网格格点到有效孔径内逐帧阵列的格林函数,并给出成像区域散射强度到数据域的映射矩阵;然后利用该区域空域稀疏的先验知识,通过正交匹配追踪的稀疏重构方式,解算出成像区域散射系数矩阵,实现了稀疏目标高分辨成像.同时,针对线性调频信号提出数据缩减的方法,通过对观测数据和字典矩阵同时脉压后截取,减小了数据规模;进一步结合二维矩阵数表查表的方法,以空间换时间,实现了区块实时成像。数值仿真以及湖试试验表明,所提算法能分辨出传统的时延求和算法难以分辨的目标,并且在图像清晰度指标上平均提升4.9 dB.改善了合成孔径声呐的成像质量.      

基于压缩感知超分辨鬼成像

分辨率是成像系统的一个重要参数, 获得高分辨率图像一直是鬼成像系统的一个目标. 本文提出了以成像系统点扩散函数作为先验知识, 基于稀疏测量的超分辨压缩感知鬼成像重建模型. 搭建了一套计算鬼成像实验装置, 用于验证该模型对于提高鬼成像系统分辨率的有效性, 并与传统的鬼成像计算模型进行了对比. 实验表明, 利用该模型可突破成像系统衍射极限分辨率的限制, 得到超分辨鬼成像. 关键词： 鬼成像 压缩感知 超分辨 稀疏测量     

基于压缩感知理论的逆合成孔径雷达抗欺骗干扰方法

针对逆合成孔径雷达(ISAR)成像对抗中的基于数字图像合成(DIS)技术欺骗干扰方法,在分析现有基于频率调制率微调的抗DIS欺骗干扰方法工程实现方面缺陷的基础上,通过将基于压缩感知理论的随机抽取采样和稀疏重构方法融入到信号接收和处理部分中,提出了基于压缩感知理论的改进抗DIS欺骗干扰方法,进一步提高基于频率调制率微调抗DIS欺骗干扰方法的工程实用性。仿真实验结果显示了基于压缩感知理论的改进抗DIS欺骗干扰方法的有效性。     

Compressed sensing sparse reconstruction for coherent field imaging

Return signal processing and reconstruction plays a pivotal role in coherent field imaging, having a significant influence on the quality of the reconstructed image. To reduce the required samples and accelerate the sampling process,we propose a genuine sparse reconstruction scheme based on compressed sensing theory. By analyzing the sparsity of the received signal in the Fourier spectrum domain, we accomplish an effective random projection and then reconstruct the return signal from as little as 10% of traditional samples, finally acquiring the target image precisely. The results of the numerical simulations and practical experiments verify the correctness of the proposed method, providing an efficient processing approach for imaging fast-moving targets in the future.     

频域稀疏采样和激光成像方法

激光的单色性和自然图像频谱稀疏且集中在低频区间的特点,使图像频谱稀疏采样成像成为可能.基于小规模激光探测器,引入参考激光,本文提出了频域稀疏采样激光成像方法.介绍了频域稀疏采样激光成像的原理和成像系统结构,推导了激光回波重构复频谱的表达式,给出了重构频谱和复图像的仿真结果并分析了信号参数对重构效果的影响,同时采用相干系数、均方误差和结构相似度来评价其重构效果.规模为256×256的激光回波复图像仿真表明, 5个拼接1/4×1/4规模频域探测器组成的近似十字型稀疏采样结构,在约31.25%(5/16)的频域稀疏采样条件下,仍可获得较好的重构频谱和重构复图像.     

Motion correction based reconstruction method for compressively sampled cardiac MR imaging

Respiratory motion during Magnetic Resonance (MR) acquisition causes strong blurring artifacts in the reconstructed images. These artifacts become more pronounced when used with the fast imaging reconstruction techniques like compressed sensing (CS). Recently, an MR reconstruction technique has been done with the help of compressed sensing (CS), to provide good quality sparse images from the highly under-sampled k-space data. In order to maximize the benefits of CS, it is obvious to use CS with the motion corrected samples. In this paper, we propose a novel CS based motion corrected image reconstruction technique. First, k-space data have been assigned to different respiratory state with the help of frequency domain phase correlation method. Then, multiple sparsity constraints has been used to provide good quality reconstructed cardiac cine images with the highly under-sampled k-space data. The proposed method exploits the multiple sparsity constraints, in combination with demon based registration technique and a novel reconstruction technique to provide the final motion free images. The proposed method is very simple to implement in clinical settings as compared to existing motion corrected methods. The performance of the proposed method is examined using simulated data and clinical data. Results show that this method performs better than the reconstruction of CS based method of cardiac cine images. Different acceleration rates have been used to show the performance of the proposed method.     

A measurement-domain adaptive beamforming approach for ultrasound instrument based on distributed compressed sensing: Initial development

High efficient acquisition of the sensor array signals and accurate reconstruction of the backscattering medium are important issues in ultrasound imaging instrument. This paper presents a novel measurement-domain adaptive beamforming approach (MABF) based on distributed compressed sensing (DCS) which seeks to simultaneously measure signals that are each individually sparse in some domain(s) and also mutually correlated with much few measurements under the Nyquist rate. Instead of sampling conventional backscattering signals at the Nyquist rate, few linear projections of the returned signal with random vectors are taken as measurements, which can reduce the amount of samples per channel greatly and makes the real-time transmission of sensor array data possible. Then high resolution ultrasound image is reconstructed from the few measurements of DCS directly by the proposed MABF algorithm without recovering the raw sensor signals with complex convex optimization algorithm. The simulated results show the effectiveness of the proposed method.     

双树小波变换与小波树稀疏联合的低场CS-MRI算法

压缩感知理论常用在磁共振快速成像上,仅采样少量的K空间数据即可重建出高质量的磁共振图像.压缩感知磁共振成像技术的原理是将磁共振图像重建问题建模成一个包含数据保真项、稀疏先验项和全变分项的线性组合最小化问题,显著减少磁共振扫描时间.稀疏表示是压缩感知理论的一个关键假设,重建结果很大程度上依赖于稀疏变换.本文将双树复小波变换和小波树稀疏联合作为压缩感知磁共振成像中的稀疏变换,提出了基于双树小波变换和小波树稀疏的压缩感知低场磁共振图像重建算法.实验表明,本文所提算法可以在某些磁共振图像客观评价指标中表现出一定的优势.     

Direction of arrival estimation for underwater acoustic target based on compressed sensing after blind reconstruction of array signal in frequency domain

The performance of direction of arrival(DOA) estimation based on compressed sensing(CS) decreases in the complex ocean marine environment.In order to tackle this problem,a method of DOA estimation for underwater acoustic target based on CS after blind reconstruction of array signal in frequency domain is proposed.Firstly,the received array data are transformed to frequency domain by Fourier transform and frequency domain wideband signal are divided into part overlapping multiple sub-band array signal.Secondly,each subband array signal are separated using plural blind source separation(BSS) method,the sub-band separated matrix and target signal can be estimated.Thirdly,the array signal in frequency domain are reconstructed according to the separated matrix and separated signals which were not noises.Fourthly,the sub-band spatial spectrum corresponding to the reconstructed array signal is obtained by CS beamforming method.Finally,the total spatial spectrum is achieved by summing the all sub-band spatial spectrum.And the target direction can be estimated by searching the peak value of the total spatial spectrum.The verification results of simulator data and sea measured data show that,under the same conditions,the target detection ability and direction precision of the proposed method is superior to the classical minimum variance distortionless response(MVDR) method,frequency domain CS method,BSS combined with MVDR method.The spatial spectrum energy of faint target signal is improved obviously,and the ability of the sonar to detect faint target is enhanced.     

结合导航回波与压缩感知进行运动伪影矫正

在一定幅度范围内,导航回波可有效追踪刚体运动,用于矫正k空间数据,从而降低运动伪影对图像质量的影响．然而导航回波技术无法很好地矫正大幅度运动以及非刚体运动导致的图像伪影,运动时刻采集到的k空间数据只能舍弃．压缩感知通过非线性规划法,对欠采样数据进行重建,能恢复出原始信号．该文采用伪随机的方式进行数据采集,结合导航回波技术,用压缩感知对未受运动影响的数据进行图像重建,从而减少运动伪影对图像的干扰．该研究为运动伪影的矫正提供了一种新思路．