基于图像压缩传感的自动式光学单点成像系统

基于图像压缩传感理论,在手动式光学单点成像系统的基础上研究了自动式光学单点成像系统。主要介绍了系统中自动编码转盘的设计以及编码块的获取,采用一系列优化的编码块图案作为测量矩阵,并利用最小均方差线性估计（MMSE）重构算法进行实验。实验研究表明,通过7.8% 低采样率即可实现对字符样本的重构。该自动编码转盘系统自动化程度较高,误差较小,而且可随意改变测量次数。     

Terahertz sensing and imaging based on nanostructured semiconductors and carbon materials

The advantageous properties of terahertz (THz) waves, such as permeability through objects that are opaque for visible light and the energy spectrum in the microelectron‐volt range that are important in materials research, allow their potential use in various applications of sensing and imaging. However, since the THz region is located between the electronic and photonic bands, even the basic components such as detectors and sources have not been fully developed, unlike in other frequency regions. THz technology also has the problem of low imaging resolution, which results from a considerably longer wavelength than that of the visible light. However, the utilization of nanostructured electronic devices has recently opened up new horizons for THz sensing and imaging. This paper provides an overview of the THz detector and imaging techniques and tracks their recent progress. Specifically, two cutting‐edge techniques, namely, frequency‐selective THz‐photon detection and integrated near‐field THz imaging, are discussed in detail. Finally, the studies of superconductors and semiconductors with high‐resolution THz imaging are described.     

Correspondence normalized ghost imaging on compressive sensing

Ghost imaging(GI) offers great potential with respect to conventional imaging techniques. It is an open problem in GI systems that a long acquisition time is be required for reconstructing images with good visibility and signal-to-noise ratios(SNRs). In this paper, we propose a new scheme to get good performance with a shorter construction time. We call it correspondence normalized ghost imaging based on compressive sensing(CCNGI). In the scheme, we enhance the signal-to-noise performance by normalizing the reference beam intensity to eliminate the noise caused by laser power fluctuations, and reduce the reconstruction time by using both compressive sensing(CS) and time-correspondence imaging(CI) techniques. It is shown that the qualities of the images have been improved and the reconstruction time has been reduced using CCNGI scheme. For the two-grayscale "double-slit" image, the mean square error(MSE) by GI and the normalized GI(NGI) schemes with the measurement number of 5000 are 0.237 and 0.164, respectively, and that is 0.021by CCNGI scheme with 2500 measurements. For the eight-grayscale "lena" object, the peak signal-to-noise rates(PSNRs)are 10.506 and 13.098, respectively using GI and NGI schemes while the value turns to 16.198 using CCNGI scheme. The results also show that a high-fidelity GI reconstruction has been achieved using only 44% of the number of measurements corresponding to the Nyquist limit for the two-grayscale "double-slit" object. The qualities of the reconstructed images using CCNGI are almost the same as those from GI via sparsity constraints(GISC) with a shorter reconstruction time.     

Efficient implementation of x-ray ghost imaging based on a modified compressive sensing algorithm

Towards efficient implementation of x-ray ghost imaging (XGI), efficient data acquisition and fast image reconstruction together with high image quality are preferred. In view of radiation dose resulted from the incident x-rays, fewer measurements with sufficient signal-to-noise ratio (SNR) are always anticipated. Available methods based on linear and compressive sensing algorithms cannot meet all the requirements simultaneously. In this paper, a method based on a modified compressive sensing algorithm with conjugate gradient descent method (CGDGI) is developed to solve the problems encountered in available XGI methods. Simulation and experiments demonstrate the practicability of CGDGI-based method for the efficient implementation of XGI. The image reconstruction time of sub-second implicates that the proposed method has the potential for real-time XGI.     

Single-pixel polarimetric imaging spectrometer by compressive sensing

We present an optical system that performs polarimetric spectral imaging with a detector with no spatial resolution. This fact is possible by applying the theory of compressive sampling to the data acquired by a sensor composed of an analyzer followed by a commercial fiber spectrometer. The key element in the measurement process is a digital micromirror device, which sequentially generates a set of intensity light patterns to sample the object image. For different configurations of the analyzer, we obtain polarimetric images that provide information about the spatial distribution of light polarization at several spectral channels. Experimental results for colorful objects are presented in a spectral range that covers the visible spectrum and a part of the NIR range. The performance of the proposed technique is discussed in detail, and further improvements are suggested.     

Terahertz imaging with compressed sensing and phase retrieval

We describe a novel, high-speed pulsed terahertz (THz) Fourier imaging system based on compressed sensing (CS), a new signal processing theory, which allows image reconstruction with fewer samples than traditionally required. Using CS, we successfully reconstruct a 64 x 64 image of an object with pixel size 1.4 mm using a randomly chosen subset of the 4096 pixels, which defines the image in the Fourier plane, and observe improved reconstruction quality when we apply phase correction. For our chosen image, only about 12% of the pixels are required for reassembling the image. In combination with phase retrieval, our system has the capability to reconstruct images with only a small subset of Fourier amplitude measurements and thus has potential application in THz imaging with cw sources.     

图-谱结合的压缩感知高光谱视频图像复原

针对液晶可调滤波片高光谱成像系统记录动态场景的成像特点,提出一种图-谱结合的压缩感知高光谱视频图像复原方法。首先,通过前景目标检测获得运动前景目标的高光谱图像,实现运动前景目标与背景区域分离,并根据前景目标检测结果将背景区域划分为运动区域(被前景目标遮挡区域)与静止区域(未被前景目标遮挡区域)。然后,基于高光谱图像空间维、光谱维相关性,对静止区域进行字典学习获得稀疏先验信息,结合压缩感知理论用于运动区域恢复,得到完整的背景区域高光谱图像。最后,将运动前景目标高光谱图像与背景区域高光谱图像相结合,得到高光谱视频图像。实验结果表明:本文提出的高光谱视频图像复原方法在峰值信噪比和视觉效果上都要优于现有算法,峰值信噪比平均提高5 d B以上。     

基于离散余弦变换的语音压缩采样和编码算法

针对语音无线通信中带宽资源受限的问题,提出基于压缩采样的低速率语音编码算法。以基尼系数为指标,比较不同稀疏变换域下语音信号的稀疏性,分析常见重构算法对语音信号压缩采样观测信号的重构特性。对标准耳蜗滤波器——伽马啁啾滤波器组的参数进行研究,并以梯度投影稀疏重建(GPSR)算法重构语音信号。利用语音质量感知评估(PESQ)、信噪比和主观听觉测试,对编解码后的合成语音信号进行了质量评估。实验表明,基于压缩感知的语音编码器以4 kbps的低速率对语音进行编码时,PESQ得分可达到3.16,计算复杂度相对较低,可以用于实际的语音编码环境。     

Photoacoustic image reconstruction based on Bayesian compressive sensing algorithm

The photoacoustic tomography (PAT) method, based on compressive sensing (CS) theory, requires that, for the CS reconstruction, the desired image should have a sparse representation in a known transform domain. However, the sparsity of photoacoustic signals is destroyed because noises always exist. Therefore, the original sparse signal cannot be effectively recovered using the general reconstruction algorithm. In this study, Bayesian compressive sensing (BCS) is employed to obtain highly sparse representations of photoacoustic images based on a set of noisy CS measurements. Results of simulation demonstrate that the BCS-reconstructed image can achieve superior performance than other state-of-the-art CS-reconstruction algorithms.     

基于阈值分割的并行压缩鬼成像方法

为了提高鬼成像的速度,提出了基于阈值分割的并行压缩差分鬼成像方法。首先通过设置合理的阈值筛选出涨落较为明显的数据进行采样;然后再通过并行分块降低图像维度,提高整体运算速度;最后通过压缩感知重构算法重构图像。通过对图"BUAA"的半物理仿真以及透射物体"光"的实验结果表明,与普通的二阶赝热光关联成像相比,该方法能以更少的采样次数和运行时间重构图像,可提升成像信噪比。     

Terahertz single pixel imaging based on a Nipkow disk

We describe a terahertz single pixel imaging system based on a Nipkow disk. Nipkow disks have been used for fast scanning imaging systems since the first experimental television was invented in 1926. In our work, a Nipkow disk with 24 scanning lines was used to provide an axial resolution of 2 mm/pixel. We also show that by implementing a microscanning technique the axial resolution can be further improved to 0.5 mm/pixel. Imaging of several objects was demonstrated to show that this simple scanning system is promising for fast or real time terahertz imaging applications.     

An infrared image super-resolution reconstruction method based on compressive sensing

Limited by the properties of infrared detector and camera lens, infrared images are often detail missing and indistinct in vision. The spatial resolution needs to be improved to satisfy the requirements of practical application. Based on compressive sensing (CS) theory, this thesis presents a single image super-resolution reconstruction (SRR) method. With synthetically adopting image degradation model, difference operation-based sparse transformation method and orthogonal matching pursuit (OMP) algorithm, the image SRR problem is transformed into a sparse signal reconstruction issue in CS theory. In our work, the sparse transformation matrix is obtained through difference operation to image, and, the measurement matrix is achieved analytically from the imaging principle of infrared camera. Therefore, the time consumption can be decreased compared with the redundant dictionary obtained by sample training such as K-SVD. The experimental results show that our method can achieve favorable performance and good stability with low algorithm complexity.     

基于压缩感知的~(252)Cf源驱动核材料浓度识别技术研究

针对252Cf源驱动噪声分析测量法中核材料浓度识别问题,采用压缩感知理论,在K最近邻(KNN)识别算法基础上,研究了一种基于压缩采样的K最近邻(CSKNN)分类识别方法,进而研究并分析了CSKNN方法的识别概率。实验结果表明,CSKNN分类识别方法只需少量的观测值(观测比M/N≥0.1),即可达到分类识别的目的;当信噪比提高时,识别概率将会以更快的速度收敛至100%,且对K值的敏感程度也会随之降低。这样,不仅提高了核军控核查的实时性,而且还有效降低了采样成本,为核材料浓度的在线判读提供了一种新的理论基础和实现方法。     

一种基于编码孔径的压缩传感图像重构算法的改进

针对压缩传感理论应用于实际系统成像时重构图像质量随图像采样率变化的问题,通过对正交匹配追踪算法进行改进,提出了一种利用空间光调制器实现编码孔径成像的压缩传感图像重构方法。该方法对传统的正交匹配追踪法迭代计算中已选入支撑集的列向量进行标记,并在下一次迭代计算中予以排除,从而减少了重构时间。在此基础上提出了将测量矩阵分别按行和按列排列进行重构后平均的图像增强算法。增强算法在达到同样重构质量时,减小了图像采样率,有利于图像数据的传输和存储。仿真实验验证了方法的有效性和稳定性,可为压缩传感技术的应用提供技术参考。     

Holographic display based on compressive sensing (Invited Paper)

We propose a method to improve the quality of the reconstructed images based on compressive sensing principles. The pseudo-inverse matrix and the total variation minimization algorithms are combined to reduce the sampling number of the computer generated hologram. Numerical simulations are performed and the results indicate that the peak signal to noise ratio is increased and the sampling ratio is decreased at the same time for holographic display.     

太赫兹大气遥感技术

由于其独特的大气敏感特性,太赫兹波在大气遥感领域起着越来越重要的作用。国际上太赫兹大气遥感技术发展方兴未艾。2004年,美国NASA发射AURA卫星,探测仪器中包括了具有两种极化的2.5 THz辐射计;2007年,欧空局ESA研制了Marschals外差式光谱仪,采用临边探测方式探测气体成分在亚毫米波段热辐射的高光谱。我国在轨气象卫星风云三号已经具备毫米波段辐射计,风云四号卫星是世界上首颗搭载太赫兹遥感仪的地球静止轨道气象卫星。针对我国大气遥感的现状,在概述国内外太赫兹遥感应用和技术的基础上,提出发展自主知识产权的大气遥感技术的思路;大力发展自主知识产权的太赫兹关键器件、太赫兹探测仪系统集成,研究太赫兹大气探测的新原理和反演新方法,整体提升我国在大气遥感领域的技术水平。     

水平阵信号压缩感知用于简正波分离

针对水平阵信号简正波分离过程中常规波束形成分辨率低以及warping模态滤波不适用于复杂声信号的问题,提出水平阵信号压缩感知用于简正波分离的方法。利用压缩感知在方位估计中的高分辨特性,通过估计水平阵接收信号在频率方位角上的二维分布,分离得到各阶简正波的方位谱,并逆Fourier变换得到时域波形。仿真孔径1 km、阵元间隔10 m水平阵接收20~200 Hz伪随机声信号和脉冲声信号,所提方法分离出的各阶简正波与理论波形的相关系数在0.97~1.0。对2011年北黄海声学实验中的海底28元水平阵接收的气枪信号,在合成至1 km孔径后使用压缩感知方法分离简正波,其与warping模态滤波分离得的前5阶简正波相关系数在0.82~0.93。仿真与实验都说明了水平阵信号压缩感知简正波分离方法的有效性。     

基于压缩感知理论的~(252)Cf源中子脉冲信号分析

针对252Cf源驱动核材料产生裂变中子脉冲信号具有脉冲序列特殊的"0,1"稀疏结构之特点,采用压缩感知理论,通过巧妙引入图论中的二分图模型,同时结合二分图的最小覆盖性质,适当添加约束条件,构建了稀疏均匀的观测矩阵。研究结果表明,利用压缩感知理论对"0,1"中子脉冲序列特殊稀疏结构的信号重构算法不仅可行,而且还获得了优于l1范数最小化方法重构结果,这对252Cf驱动核材料的中子脉冲信号分析与处理提供了一种新的途径或方法。     

Fresnel hologram reconstruction of complex three-dimensional object based on compressive sensing

Reconstruction the computer generated Fresnel hologram of complex 3D object based on compressive sensing （CS） is presented. The hologram is synthesized from a color image and the depth map of the 3D object. With the depth map, the intensity of the color image can be divided into multiple slices, which satisfy the condition of the sparsity of CS. Thus, the hologram can be reconstructed at different distances with corresponding scene focused using the CS method. The quality of the recovered images can be greatly improved compared with that from the back-propagation method. What＇s more, with the sub-sampled hologram, the image can be ideally reconstructed by the CS method, which can reduce the data-rate for transmission or storage.     

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.