确定性相位掩膜可压缩双透镜成像

压缩成像是压缩传感理论的一个重要应用领域.本文将确定性测量引入压缩成像,提出一种确定性相位掩膜可压缩双透镜成像方法.模拟实验结果表明,新的成像方法可以在显著地降低物理实现成本的同时,有效地捕获图像信息来重建原始图像.此方法改变了经典的模拟-数字转换的光学成像思路,减少模数转换开销,并有利于图像的传输和存储,可以为照相机...     

确定性相位掩膜可压缩双透镜成像

压缩成像是压缩传感理论的一个重要应用领域.本文将确定性测量引入压缩成像,提出一种确定性相位掩膜可压缩双透镜成像方法.模拟实验结果表明,新的成像方法可以在显著地降低物理实现成本的同时,有效地捕获图像信息来重建原始图像.此方法改变了经典的模拟-数字转换的光学成像思路,减少模数转换开销,并有利于图像的传输和存储,可以为照相机的设计提供若干理论、计算和技术支撑.     

应用CS理论实现同步采样压缩成像

为了减轻图像数据存储负担,实现图像在网络上的快速传输和实时处理,对一种新的压缩传感(CS)理论进行了研究。介绍了压缩传感理论的主要思想和基于压缩传感理论的光学成像系统,给出了一种新型图像重建算法—和谐正交匹配追踪算法,并进行了相应的模拟实验。实验结果显示,该成像机制可同步完成图像的采样与数据压缩,同时可获得良好的图像重建效果。由于该方法所要传输的信号数据量较小,所以十分有利于远距离的图像传输。     

托普利兹-循环块相位掩膜矩阵压缩成像

在托普利兹和循环矩阵的基础上,提出一种新的托普利兹-循环块相位掩膜矩阵可压缩双透镜成像方法.模拟实验结果表明:新的相位掩膜矩阵压缩成像可以在显著减少测量的同时,有效地捕获图像信息来重建原始图像|新相位掩膜矩阵的研究为确定性测量在压缩成像领域的应用提供了更多的支撑,在拥有原托普利兹和循环确定性测量优点的同时,还拥有自身的块结构特点,可以进一步减少物理实现成本,为新的照相机的设计提供若干理论、计算和技术支撑.     

循环-托普利兹块相位掩模可压缩双透镜成像

压缩成像是压缩传感理论的重要应用领域之一,可以用比Nyquist测量数目少的测量值捕获充分信息重建稀疏或可压缩图像.在研究现有的压缩成像方法的基础上,给出一种新的循环-托普利兹块相位掩模矩阵可压缩双透镜成像方法.模拟实验结果表明新的相位掩模矩阵成像方法可以在欠采样的情况下有效地获得图像信息来重建原始图像.新方法的研究为...     

托普利兹-循环块相位掩膜矩阵压缩成像

在托普利兹和循环矩阵的基础上,提出一种新的托普利兹-循环块相位掩膜矩阵可压缩双透镜成像方法,模拟实验结果表明:新的相位掩膜矩阵压缩成像可以在显著减少测量的同时,有效地捕获图像信息来重建原始图像;新相位掩膜矩阵的研究为确定性测量在压缩成像领域的应用提供了更多的支撑,在拥有原托普利兹和循环确定性测量优点的同时,还拥有自身的...     

基于高斯混合尺度模型的压缩传感图像重构

在图像处理领域中,压缩传感重构是稀疏表示下的最重要的病态反问题之一。压缩传感图像重构利用图像可稀疏表示的先验知识,从比奈奎斯特采样率低得多的随机投影观测值中重构原始图像。为了克服传统的压缩传感算法中收敛速度慢和未利用变换系数的邻域统计特性的缺点,提出了基于高斯混合尺度模型的压缩传感图像重构算法,证明了独立的高斯混合尺度分布作为压缩传感重构的稀疏先验知识的可行性,结合全变差调整进一步提高算法的性能。实验结果表明,该算法有效地提高了重构图像的主观视觉效果和峰值信噪比,加快了压缩传感图像重构算法的收敛速度。     

随机间距稀疏三元Toeplitz矩阵相位掩模压缩成像

压缩成像是一种基于压缩传感(CS)理论的新成像方法,其优点是可以用比传统的Nyquist采样定理所需测量数目少得多的测量值重建原稀疏或可压缩图像。在研究Bernoulli和Toeplitz测量矩阵的基础上,提出一种新的随机间距稀疏三元Toeplitz相位掩模矩阵。实验结果表明,在可压缩双透镜成像系统中,与Bernoulli和Bernoulli-Toeplitz相位掩模矩阵相比,新相位掩模矩阵的成像信噪比与之相当。但是随机独立变元个数和非零元个数显著减少,在数据存储与传输时更具优势,物理上更易实现,甚至重建时间是只有它们的21%～66%。     

基于压缩传感和代数重建法的CT图像重建

代数重建法(ART)是一种重要的CT图像重建方法,适合于不完全投影数据的图像重建,其缺点是重建速度慢。为提高图像重建的质量和速度,利用压缩传感理论提出了一种基于ART的高质量图像重建算法。该算法将CT图像的梯度稀疏性结合到ART图像重建中,在每次迭代中的投影操作结束后用梯度下降法调整全变差,减小图像梯度的l1范数。实验结果验证了该算法的有效性。     

基于改进的分块压缩感知红外图像重建

针对基于压缩感知理论的红外图像重建问题,提出一种基于改进的分块压缩感知红外图像重建方法。该方法首先对原始红外图像进行分块,并对每个子块用相同的观测矩阵进行随机观测,获得少量的观测数据;然后利用谱图小波变换优异的稀疏特性,将其引入平滑投影Landweber算法进行迭代优化重建,同时采用混合中值滤波进行处理以增加图像的平滑度和减少块伪影,最后输出满足要求的高质量红外图像。实验结果表明,在相同采样率下,该方法对于不同类型红外图像的重建性能均优于目前广为采用的一些小波压缩感知方法,可获得更高质量的红外图像。     

基于网络层析成像的光网络流量矩阵估计方法(英文)

提出一种面向光传输网络的流量矩阵估计方法.采用压缩感知理论研究光传输网络中的流量矩阵估计,根据信号稀疏表示将流量矩阵稀疏化,基于矩阵变换理论提出新的面向光传输网络的网络层析成像模型.该模型克服了已有网络层析成像模型的病态特性,并通过凸优化来获得流量矩阵的估计等式.提出了具体的估计算法,获得关于光传输网络流量矩阵的精确估计.真实网络的数据仿真表明所提出的方法是有效和可行的.     

Secure collaborative cognitive radio based on chaotic modulation and compressive sensing

Cognitive radio (CR) is a wireless technology that is used to overcome the spectrum scarcity problem. CR includes several stages, spectrum sensing is the first stage in the CR cycle. Traditional spectrum sensing (SS) techniques have many challenges in the wideband spectrum. CR security is an important problem, since when an attacker from outside the network access the sensing information this produces an increase in sensing time and reduces the opportunities for exploiting vacant band. Compressive sensing (CS) is proposed to capture all the wideband spectrum at the same time to solve the challenges and improve the performance in the traditional techniques and then one of the traditional SS techniques are applied to the reconstructed signal for detection purpose. The sensing matrix is the core of CS must be designed in a way that produces a low reconstruction error with high compression. There are many types of sensing matrices, the chaotic matrix is the best type in terms of security, memory storage, and system performance. Few works in the literature use the chaotic matrix in CS based CR and these works have many challenges: they used sample distance in the chaotic map to generate a chaotic sequence which consumes high resources, they did not take into consideration the security in reporting channel, and they did not measure their works using real primary user (PU) signal of a practical application under fading channel and low SNR values. In this paper, we propose a chaotic CS based collaborative scenario to solve all challenges that have been presented. We proposed a chaotic matrix based on the Henon map and use the differential chaotic shift keying (DCSK) modulation to transmit the measurement vector through the reporting channel to increase the security and improve the performance under fading channel. The simulation results are tested based on a recorded real-TV signal as PU and Compressive Sampling Matching Pursuit (CoSaMP) recovery algorithm under AWGN and TDL-C fading channels in collaborative and non-collaborative scenarios. The performance of the proposed system has been measured using recovery error, mean square error (MSE), derived probability of detection (Pdrec), and sensitivity to initial values. To measure the improvement introduced by the proposed system, it is evaluated in comparison with selected chaotic and random matrices. The results show that the proposed system provides low recovery error, MSE, with high Pdrec, security, and compression under SNR equal to −30 dB in AWGN and TDL-C fading channels as compared to other matrices in the literature.     

Novel hybrid image compression–encryption algorithm based on compressive sensing

A new hybrid image compression–encryption algorithm based on compressive sensing is proposed, which can accomplish image encryption and compression simultaneously. The partial Hadamard matrix is adopted as measurement matrix, which is controlled by chaos map. The measurement is scrambled. Compared with the methods adopting the Gaussian random matrix as measurement matrix, and those using the whole measurement matrix as key, the proposed algorithm reduces the burden of transferring key and is more practical. The proposed algorithm with sensitive keys and nice image compression ability can resist various attacks. Simulation results verify the validity and reliability of the proposed algorithm.     

Experimental investigation of the quality of lensless super-resolution ghost imaging via sparsity constraints

Ghost imaging via sparsity constraints (GISC) can nonlocally realize super-resolution imaging. Factors influencing the quality of lensless super-resolution GISC are investigated and the experimental results show that, the quality of GISC is enhanced as the object?s sparse ratio in the representation basis or the spatial transverse coherence lengths on the object plane are decreased. The differences between ghost imaging (GI) and GISC are also discussed.     

Single image non-uniformity correction using compressive sensing

A non-uniformity correction (NUC) method for an infrared focal plane array imaging system was proposed. The algorithm, based on compressive sensing (CS) of single image, overcame the disadvantages of “ghost artifacts” and bulk calculating costs in traditional NUC algorithms. A point-sampling matrix was designed to validate the measurements of CS on the time domain. The measurements were corrected using the midway infrared equalization algorithm, and the missing pixels were solved with the regularized orthogonal matching pursuit algorithm. Experimental results showed that the proposed method can reconstruct the entire image with only 25% pixels. A small difference was found between the correction results using 100% pixels and the reconstruction results using 40% pixels. Evaluation of the proposed method on the basis of the root-mean-square error, peak signal-to-noise ratio, and roughness index (ρ) proved the method to be robust and highly applicable.     

Downlink channel estimation in FDD cell-free massive MIMO

In frequency-division duplexing (FDD) cell-free massive multiple-input multiple-output (MIMO) systems, an excessive channel estimation overhead is a critical issue that limits the system performance. In this paper, by exploiting the sparse channel characteristics of such a cell-free system, we apply compressive sensing to estimate the channel state information and solve the excessive pilot overhead problem. The proposed algorithm estimates several channel coefficients with significant gains in the power domain and ignores the approximately zero coefficients. Compared to minimum mean square error (MMSE) estimation with orthogonal pilots, the proposed method significantly reduces the pilot overhead in an FDD cell-free massive MIMO system. The access points (APs) that contribute low gains feature reduced energy consumption because the power coefficients corresponding to zero gains in the sparse channel are assigned zeros in the power control process. Therefore, to improve the energy efficiency, the ignored channel coefficients reduce the power overhead.     

基于相位一致性的实时压缩跟踪方法

针对基于压缩感知的目标跟踪算法在跟踪过程中,光照剧烈变化引起跟踪不稳定或跟踪失败的问题,本文提出了一种基于相位一致性的改进跟踪方法.该方法利用相位一致性图像特征对光照变化不敏感的特点,首先对样本搜索区域内的图像进行相位一致性变换,然后再提取变换后相位一致性图像的特征,将其用于分类器中来确定目标位置.实验结果表明,该方法在目标受到光照剧烈变化影响的情况下具有很强的适应性,在目标大小为50pixel×55pixel时平均处理帧频可达22fps.与已有基于压缩感知跟踪算法相比,该算法在光照变化剧烈的情况下仍具有很好的鲁棒性,而且在目标尺度和纹理发生一定变化的情况下跟踪稳定.