基于压缩感知的差分关联成像方案研究

关联成像可提供一种运用常规手段难以获得清晰图像的方法, 能够解决一些常规成像技术不易解决的问题, 是近些年来量子光学领域的前沿和热点之一.本文提出一种基于压缩感知的差分关联成像方案(简称, 差分压缩关联成像方案), 将高斯分布的热光源强度分布作为压缩感知的测量矩阵, 差分物体信息作为被成像物体信息, 利用差分关联成像方案的高成像信噪比和压缩感知技术的低采样次数, 通过正交匹配追踪算法, 高质量地恢复出物体信息. 并以二灰度“双缝”、“NUPT”, 多灰度Lena图和Boats图为例, 数值仿真差分压缩关联成像过程; 同时将本方案350次测量的结果与差分关联成像方案30000次测量的结果进行对比, 研究结果表明针对不同的被成像物体(二灰度“双缝”、“NUPT”, 以及多灰度Lena图和Boats图), 10次成像的均方误差平均值分别降低了97.7%, 93.9%, 92.5%和71.4%; 与压缩鬼成像方案相比, 同样测量次数条件下均方误差值对于二灰度双缝和多灰度Lena图、Boats图等目标物 体分别有50.4%, 72.9%和66.8%的降低. 差分压缩关联成像方案极大地提高了成像信噪比, 降低了成像时间. 关键词： 关联成像 差分 压缩感知 均方误差     

Compressive imaging based on multi-scale modulation and reconstruction in spatial frequency domain

Imaging quality is a critical component of compressive imaging in real applications. In this study, we propose a compressive imaging method based on multi-scale modulation and reconstruction in the spatial frequency domain. Theoretical analysis and simulation show the relation between the measurement matrix resolution and compressive sensing(CS)imaging quality. The matrix design is improved to provide multi-scale modulations, followed by individual reconstruction of images of different spatial frequencies. Compared with traditional single-scale CS imaging, the multi-scale method provides high quality imaging in both high and low frequencies, and effectively decreases the overall reconstruction error.Experimental results confirm the feasibility of this technique, especially at low sampling rate. The method may thus be helpful in promoting the implementation of compressive imaging in real applications.     

Double-threshold technique for achieving denoising in compressive imaging applications

Single-pixel cameras, which employ either structured illumination or image modulation and compressive sensing algorithms, provide an alternative approach to imaging in scenarios where the use of a detector array is restricted or difficult because of cost or technological constraints. In this work, we present a robust imaging method based on compressive imaging that sets two thresholds to select the measurement data for image reconstruction.The experimental and numerical simulation results show that the proposed double-threshold compressive imaging protocol provides better image quality than previous compressive imaging schemes. Faster imaging speeds can be attained using this scheme because it requires less data storage space and computing time. Thus,this denoising method offers a very effective approach to promote the implementation of compressive imaging in real-time practical applications.     

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.     

两种相似塑料材料的显微近、中红外成像方法研究

显微近、中红外成像不仅可以获得样品的光谱信息,而且可以获得样品的空间分布信息,这是传统的近、中红外光谱分析所无法比拟的。该文以外观非常相似的聚乙烯膜(材料Ⅰ)和封口膜(材料Ⅱ)为研究对象,分别采集了样品的显微近、中红外图像。针对两种材料进行化学成像和相关光谱成像,比较并讨论了每种材料的两种成像方法。结果表明,材料Ⅱ的显微近、中红外化学成像中,两种材料化学成像值相差分别为0.004 8和0.254 8;材料Ⅰ的显微近、中红外化学成像中,两种材料化学成像值相差分别为0.002 6和0.326 5;近、中红外谱区的显微成像皆可得到两种材料清晰的成像,从而可区分两种材料。对两种材料相关光谱成像的研究表明,分别以两种材料的近、中红外光谱作为参比光谱的相关光谱成像可以明显地区分两种材料,成像结果较清晰;显微中红外相关光谱成像中,两种材料的光谱和参比光谱的相关系数差异大于0.12,成像结果更清晰;而显微近红外相关光谱成像图可利用图像中两种材料光谱和参比光谱相关系数的细微差异区分两种材料。该研究为农产品包装材料安全性的快速判别提供一定的参考,并为显微近、中红外成像分辨不同材料提供一定的成像方法参考。     

Conditions for practicing compressive Fresnel holography

Recent works have applied diffraction-based wave propagation for compressive imaging applications. In this Letter, we derive the theoretical bounds on the performance of compressive imaging systems based on Fresnel wave propagation, and we show that it is related to the imaging sensor's physical attributes, illumination wavelength, and working distance.     

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...     

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.     

基于lp范数的压缩感知图像重建算法研究

图像重建是光学成像、光声成像、声纳成像、核磁共振成像、 天体成像等物理成像领域中的关键技术之一. 近年来提出的压缩感知理论指出: 对稀疏或者可压缩信号进行少量非自适应线性投影,投影信号含有足够的信息, 从而能对信号进行高概率重建. 压缩感知已被应用于多种物理成像系统. 将罚函数法和修正Hesse阵序列二次规划方法相结合, 并采用了分块压缩感知思想, 提出一种基于l_p范数的压缩感知图像重建算法. 以cameraman, barbara和mandrill图像为例, 采用该算法进行图像重建. 首先, 在不同采样率下对图像重建. 即便采样率低至0.3时, 也能获得高达32.23dB的信噪比, 重建图像清晰可辨. 验证了该算法的正确性. 其次, 将该算法与正交匹配追踪算法进行对比, 在采样率达到0.5以上时, 能够获得高信噪比的重建图像, 成像时间也大为减少, 特别是采样率为0.7时, 成像时间减少88%. 最后, 与现有基于l_p 范数的压缩感知图像重建算法进行对比, 计算结果表明在成像质量有所提高的基础上, 成像时间大为缩短. 关键词： 图像重建 压缩感知 罚函数 修正Hesse阵序列二次规划     

窄带高光谱干涉成像的压缩采样复原方法

利用干涉成像光谱仪对目标进行窄带高光谱成像探测具有高光通量、高光谱分辨率和高目标分辨率等优点。按照尼奎斯特定理对窄带光谱干涉信息进行采样存在较大的数据冗余,增加了后期傅里叶变换的数据处理量,影响光谱的复原效率。在分析窄带光谱傅里叶变换特性的基础上,提出了基于滤光片光谱透射率函数的窄带光谱压缩采样方法。引入滤光片参数和混叠参数,可以复原不同精度的窄带光谱信息。配以符合要求的多带通窄带滤光片,可对目标进行压缩采样获取多个谱段的窄带光谱信息,从而避免了逐个谱段探测,提高了探测效率。对该方法进行了仿真分析和实验验证,得到了与目标光谱相吻合的复原窄带光谱。     

Block-wise motion detection using compressive imaging system

A block-wise motion detection strategy based on compressive imaging, also referred to as feature-specific imaging (FSI), is described in this work. A mixture of Gaussian distributions is used to model the background in a scene. Motion is detected in individual object blocks using feature measurements. Gabor, Hadamard binary and random binary features are studied. Performance of motion detection methods using pixel-wise measurements is analyzed and serves as a baseline for comparison with motion detection techniques based on compressive imaging. ROC (Receiver Operation Characteristic) curves and AUC (Area Under Curve) metrics are used to quantify the algorithm performance. Because a FSI system yields a larger measurement SNR(Signal-to-Noise Ratio) than a traditional system, motion detection methods based on the FSI system have better performance. We show that motion detection algorithms using Hadamard and random binary features in a FSI system yields AUC values of 0.978 and 0.969 respectively. The pixel-based methods are only able to achieve a lower AUC value of 0.627.     

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.     

Increasing FTIR spectromicroscopy speed and resolution through compressive imaging

At the Advanced Light Source at Lawrence Berkeley National Laboratory, we are investigating how to increase both the speed and resolution of synchrotron infrared imaging. Synchrotron infrared beamlines have diffraction-limited spot sizes and high signal to noise, however spectral images must be obtained one point at a time and the spatial resolution is limited by the effects of diffraction. One technique to assist in speeding up spectral image acquisition is described here and uses compressive imaging algorithms. Compressive imaging can potentially attain resolutions higher than allowed by diffraction and/or can acquire spectral images without having to measure every spatial point individually thus increasing the speed of such maps. Here we present and discuss initial tests of compressive imaging techniques performed with ALS Beamline 1.4.3’s Nic-Plan infrared microscope, Beamline 1.4.4 Continuum XL IR microscope, and also with a stand-alone Nicolet Nexus 470 FTIR spectrometer.     

化学成像技术在手印显现和增强中的应用

化学成像技术结合光谱分析和成像技术从而同时获得物质的光谱和空间各点的组成和结构信息。振动光谱方法(红外和拉曼光谱)与成像相结合,具有灵敏、快速、无损检验等优点,能够为检验鉴定提供定性定量的准确信息,近年来在物证鉴定领域获得了重要的应用。手印的显现和增强有多种方法,化学成像技术作为一种潜力巨大的方法可以显现多种客体上的潜在手印而不需要任何前处理。该技术还可以增强用其他方法显现后的手印,使之与背景形成较大反差。随着成像仪器的发展,化学成像技术在手印显现领域的应用将会一步拓展。文章介绍了化学成像技术的基本原理和仪器,综述了化学成像技术在潜在手印的显现和增强方面的具体应用,展望了化学成像技术的发展前景。     

Bayesian compressive sensing for thermal imagery using Gaussian-Jeffreys prior

Recent advances have shown a great potential to explore compressive sensing (CS) theory for thermal imaging due to the capability of recovering high-resolution information from low-resolution measurements. In this paper, we present a Bayesian CS reconstruction algorithm that makes use of a new sparsity-inducing prior, referred as Gaussian-Jeffreys prior, and demonstrate performance gain of imposing this new prior on thermal imagery where the signal-to-noise ratio is low. We first derive a hierarchical representation of the Gaussian-Jeffreys prior that facilitates computational tractability, then propose an efficient evidence approximation inference algorithm. We show that the proposed estimator is able to provide stronger sparsity-inducing power comparing to the conventional choices. Extensive numerical examples are provided with performance comparisons of different CS estimators, in particular when the compressive measurements are available via thermal imaging.     

Computational Spectral Imaging Based on Compressive Sensing

Spectral imaging is an important tool for a wide variety of applications. We present a technique for spectral imaging using computational imaging pattern based on compressive sensing(CS). The spectral and spatial information is simultaneously obtained using a fiber spectrometer and the spatial light modulation without mechanical scanning. The method allows high-speed, stable, and sub sampling acquisition of spectral data from specimens.The relationship between sampling rate and image quality is discussed and two CS algorithms are compared.     

Terahertz two-pixel imaging based on complementary compressive sensing

A compact terahertz(THz) imaging system based on complementary compressive sensing has been proposed using two single-pixel detectors. By using a mechanical spatial light modulator, sampling in the transmission and reflection orientations was achieved simultaneously, which allows imaging with negative mask values. The improvement of THz image quality and anti-noise performance has been verified experimentally compared with the traditional reconstructed image, and is in good agreement with the numerical simulation. The demonstrated imaging system, with the advantages of high imaging quality and strong anti-noise property, opens up possibilities for new applications in the THz region.     

针对多散斑图的差分压缩鬼成像方案研究

鬼成像方案实现所需设备、成像的质量以及成像所花的时间是决定鬼成像技术可实用化的重要因素. 本文提出一种针对多散斑图的差分压缩鬼成像方案. 该方案通过连续探测多个独立的散斑图, 降低了热光鬼成像方案对探测器高时间分辨力的要求; 通过采用差分方法, 抑制了背景噪声和其他噪声源的干扰; 通过使用压缩感知重建算法, 有效地降低了鬼成像所需时间并同时提升成像的质量. 数值仿真结果表明, 对于二灰度“N” 图, 本方案在8000次的采样情形下与多散斑图鬼成像方案35000次采样的结果相比, 均方误差降低了96.9%、峰值信噪比提升15.1 dB. 对于八灰度“Pepper”图, 本方案与多散斑图鬼成像方案相比, PSNR提升11.4 dB. 本方案可降低探测设备的要求、提高成像质量、降低重建时间, 具有广阔的应用前景.     

基于光子计数的合作目标“量子”成像

在实验上研究了赝热光照明下, 基于光子计数模式的合作目标“量子”成像, 并给出理论模型和解释. 研究表明, 利用光子计数的单光子探测器代替以往光强度线性探测器作为桶探测器在“量子”成像中同样适用. 实验发现, 合 作目标的反射信号可穿透弱散射介质实现成像, 该技术在减小光学成像透镜孔径方面具有潜在的应用价值. 对比了基于强度关联成像和压缩感知算法的“量子”成像结果, 并得出实用性结论. 本文的方案为“量子”成像的实际应用提供了新方法.     

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。