光子计数模式下的目标探测与成像

当光微弱到以单个光子发射时,成像系统只有利用光子计数模式才能探测到单光子信息。采用基于碰撞电离效应的全固态雪崩光电二极管作为探测元件,构成微光环境下的光子计数成像实验系统。该系统的硬件主要由雪崩光电二极管构成的单光子计数器、计算机、微光照度计、2维电控导轨、控制器、暗箱等组成。控制器的软件在Altera公司Quartus环境下设计,主要完成导轨运动的控制;上位机软件采用VC++编程实现系统的数据采集处理、系统功能控制和光子计数图像显示等。该系统为全固态结构,工作电压小于35 V,暗计数率小于4 Hz。所建光子计数成像系统在10－5 lx微光环境下实现了目标的探测成像。     

光子计数模式下的目标探测与成像

 当光微弱到以单个光子发射时,成像系统只有利用光子计数模式才能探测到单光子信息。采用基于碰撞电离效应的全固态雪崩光电二极管作为探测元件,构成微光环境下的光子计数成像实验系统。该系统的硬件主要由雪崩光电二极管构成的单光子计数器、计算机、微光照度计、2维电控导轨、控制器、暗箱等组成。控制器的软件在Altera公司Quartus环境下设计,主要完成导轨运动的控制;上位机软件采用VC++编程实现系统的数据采集处理、系统功能控制和光子计数图像显示等。该系统为全固态结构,工作电压小于35 V,暗计数率小于4 Hz。所建光子计数成像系统在10－5 lx微光环境下实现了目标的探测成像。     

Design of correlation filters for pattern recognition using a noisy reference

We present the design of correlation filters for detection of a target in a noisy input scene when the object of interest is given in a noisy reference image. The target signal, shape and location in the reference image are assumed to be unknown. Two signal models are considered for the input scene: additive and nonoverlapping. The design of the filters consists of automated estimation of needed parameters from a noisy reference image and maximization of the peak-to-output energy ratio criterion. Two filter variants are proposed. The matching error metric is used to determine the regions of the parameter space where each filter variant performs better. Computer simulation results obtained with the proposed filters are presented and evaluated in terms of discrimination capability, location errors, and tolerance to input noise.     

High efficiency photon counting using stored light

We propose a method that can, in principle, achieve nearly ideal photon counting, by combining the techniques of photonic quantum memory and ion-trap quantum-state measurements. After mapping the quantum state of a propagating light pulse onto metastable collective excitations of a trapped cold atomic gas, it is possible to monitor the resonance fluorescence induced by an additional laser field that couples only to the metastable excited state. Even with a photon collection/detection efficiency as low as 10%, it is possible to achieve photon counting with efficiency exceeding 99%.     

光学相关器在自动目标识别中的应用

为使光学相关器更好地应用于自动目标识别领域,保证在较高识别速度下,具备良好的识别效果,介绍了应用于自动目标识别领域的联合变换相关器和匹配滤波相关器.针对匹配滤波相关器实现过程中的两项关键技术,在滤波器编码技术上提出了构造等相关峰综合鉴别函数以解决畸变不变识别问题.在相关峰识别技术上,针对阈值法存在的不足,提出利用BP神经网络对相关平面相关峰进行识别,采用划分感兴趣区域的方法减少了输入层神经元数目,简化了神经网络结构.借助搭建的光学相关器系统验证了该方法可对相关信号和噪声进行有效分类,提高了光学相关器的识别效果.     

Critical adjustment of correlation filters for optical character recognition

As yet, optical correlation filters were not applied successfully in commercially available equipment since 1964. Since Vander Lugt¹ prepared the first correlation filter using a Mach Zender interferometer a considerable amount of effort both experimental and theoretical was devoted to them.

In this paper an attempt will be made to evaluate their usefulness in optical character recognition on the basis of our experimental work. No attempt will be made to survey other work in this field.

For the sake of completeness a brief outline of the theory will be given.     

Three-dimensional photon counting integral imaging using moving array lens technique

In this Letter, we present three-dimensional (3D) photon counting integral imaging using the moving array-lens technique (MALT) to improve the visualization of a reconstructed 3D scene. In 3D scene reconstruction of photon counting integral imaging, various techniques such as maximum likelihood estimation may be used. However, the visual quality depends on the number of scene photons or detector pixels activated by photons. We show that MALT may improve the viewing resolution of integral imaging for reconstructed 3D scene under photon-starved conditions.     

水声被动目标识别技术挑战与展望

低频水声探测和船舶减振降噪技术发展,使得传统水声目标识别技术性能逐渐下降。该文分析了声呐工作带宽、探测频率、船舶减振降噪给识别技术带来的挑战。针对低频声呐广泛使用的低频线谱识别,研究了低频线谱的识别能力问题;针对智能识别技术发展,研究了深度学习技术在船舶辐射噪声识别中的应用问题,并给出了数据试验结果,文章最后指出了水声被动目标识别技术亟需开展的研究内容和方向。     

基于多通道Gabor滤波器的高鲁棒灰度图像目标识别新方法

提出了一种针对低质量的灰度图像的基于多通道Gabor小波滤波器的高鲁棒目标识别新方法。主要是利用Gabor小波设计了滤波器,滤波器的中心频率是一个从低到高的范围。滤波器采用不同方向、不同尺度,从而组成多通道滤波器。对灰度图像直接进行小波变换,用Gabor小波变换系数的模的平均值和其标准方差来表示抽取的灰度图像目标的特征,并对获得的小波特征归一化后输入到改进的BP神经网络分类器中进行分类识别。对四种不同的飞机灰度图像目标进行了分类识别仿真实验。结果表明,这种特征提取方法能有效地提取灰度图像目标纹理特征,并且对噪音和形状的变化具有强鲁棒性。在应用灰度图像对目标进行识别时,神经网络的训练时间减少到10min,识别率达到94%。     

用于畸变目标相关识别的匹配滤波器设计

针对尺度缩放和角度旋转变化目标相关识别率低的问题,采用优化折中综合鉴别函数方法设计了具有较大畸变容限的匹配滤波器。在匹配滤波器设计时,通过调整相关输出噪声、训练样本平均功率谱密度和训练样本相似度对训练样本平均值的贡献,得到具有带通性质的匹配滤波器。利用单个匹配滤波器对角度旋转0°～30°、尺度变化0～20%的目标进行了相关识别仿真实验。结果表明：这种匹配滤波器相关输出峰尖锐、鉴别力强、畸变容限大,可用于畸变目标的光学相关识别。     

Three-dimensional color photon counting microscopy using Bayesian estimation with adaptive priori information

In this Letter, we propose a novel three-dimensional(3D) color microscopy for microorganisms under photonstarved conditions using photon counting integral imaging and Bayesian estimation with adaptive priori information. In photon counting integral imaging, 3D images can be visualized using maximum likelihood estimation(MLE). However, since MLE does not consider a priori information of objects, the visual quality of 3D images may not be accurate. In addition, the only grayscale image can be reconstructed. Therefore, to enhance the visual quality of 3D images, we propose photon counting microscopy using maximum a posteriori with adaptive priori information. In addition, we consider a wavelength of each basic color channel to reconstruct 3D color images. To verify our proposed method, we carry out optical experiments.     

3D photon counting imaging at 1550 nm with 1.5-GHz sine-wave-gated InGaAs/InP GAPD

We demonstrated a 3D laser imaging system at 1550 nm with a 1.5-GHz sine-wave gated Geiger-mode InGaAs/InP avalanche photodiode (APD). An optical fiber bundle with 100 individual fiber outputs was implemented at the focal plane of the telescope, providing a 2.5-mrad imaging view. The system used single-pixel near-infrared single-photon detector to measure photons at fiber outputs instead of a photon counting array. The 1.5-GHz gated Geiger-mode InGaAs/InP APD with a timing jitter of 290 ps was operated in quasi-continuous mode with detection efficiency of ∼4.3%. We achieved higher than 6-cm surface-to-surface resolution at single-photon level, showing a potential of low-energy and eye-safe laser imaging system for long-distance measurements.     

使用深度学习的多通道水下目标识别

为解决低信噪比条件下水下目标识别率低的问题,提出一种适用于多通道水听器阵列的深度学习水下目标识别方法。首先是采用子通道特征级联的方法利用多通道信息;在特征提取方面,采用对信号的不同频率区间进行加权的特征提取器,并对提取的特征进行正则规整;最后采用深度神经网络(Deep Neural Network,DNN)实现目标识别。实验首先在仿真条件下对所提出方法的有效性进行验证,结果表明在-15 dB信噪比条件下的五目标识别任务中,使用多通道级联特征的深度神经网络的识别正确率达到96.7%,显著高于基于支持向量机(Support Vector Machine,SVM)的方法。在后续的湖上试验中,深度神经网络的平均正确率达到96.0%,进一步验证了所提出方法的有效性。     

Evaluation of polarization entanglement generated by spontaneous parametric downconversion using photon number counting

Spontaneous parametric downconversion (SPDC) is widely used to generate entangled photon pairs; however, multi-pair emissions degrade the quality of the entanglement. We numerically evaluate polarization-entangled photon pairs created by SPDC. The effects of multi-pair emission events on the visibility of two-photon interference and on the fidelity (the probability overlap for ideal and real states) are analyzed using single-photon detectors that can count the number of incoming photons and discard multiphoton events. Compared with conventional threshold single-photon detectors, photon-number resolving single-photon detectors have higher fidelity for the same or lower visibility.     

利用多途时延的被动目标定位方法

本文研究了在海洋波导环境中利用多途时延信息进行水下目标被动定位的方法。理论分析了在等声速理想海洋波导环境中,多途时延信息随时间变化的规律及其与目标运动状态之间的联系。分析结果表明,不需要已知声线类型而仅利用多途时延信息即可实现浅海波导环境中水下目标的被动定位。利用多途时延信息,当有两条声线到达时可以估计运动目标的归一化径向速度;当有三条声线到达时,可实现水下运动目标距离、径向速度的估计。最后利用仿真和实验数据对上述结论进行了验证。     

Rotation-invariant pattern recognition using morphological phase-only correlation

We present a rotation-invariant nonlinear correlator based on the circular harmonic filter (CHF) and the previously proposed morphological phase-only correlator (MPC) [Q. Wang, S. Liu, Opt. Commun. 244 (2005) 93]. We refer to this correlator as a rotation-invariant MPC (RIMPC). Through computer simulation, we compare the output results of RIMPC with those of rotation-invariant MC (RIMC) and CHF when input scene is corrupted by salt-and-pepper noise, white additive Gaussian noise and cluttered background. Our results show that RIMPC yields higher discriminability, sharper and higher correlation peaks and displays better stability against the above three kinds of noise than do the RIMC and common CHF.     

Morphological self-organizing feature map neural network with applications to automatic target recognition

The rotation invariant feature of the target is obtained using the multi-direction feature extraction property of the steerable filter. Combining the morphological operation top-hat transform with the self-organizing feature map neural network, the adaptive topological region is selected. Using the erosion operation, the topological region shrinkage is achieved. The steerable filter based morphological self-organizing feature map neural network is applied to automatic target recognition of binary standard patterns and realworld infrared sequence images. Compared with Hamming network and morphological shared-weight networks respectively, the higher recognition correct rate, robust adaptability, quick training, and better generalization of the proposed method are achieved.     

Determination of Particle Size Distribution in Fluids using photon correlation spectroscopy

Photon correlation spectroscopy (PCS) has been applied to various systems of particles suspended in a fluid. Computer simulations were used to analyse CONTIN, a program for the numerical inversion of the experimental data. Measurements were performed on a spectrometer equipped with a digital correlator. Various suspensions of polysterene latex spheres differing both in size and size distribution were prepared in double distilled water. Further some samples provided by an external institution without prior specification were analysed. Diffusivity measurements on submicron DES (di-2-ethylhexyl sebacate) particles in nitrogen were also performed. The results obtained demonstrate the strengths and the limitations of PCS as a method of determining particle size distributions in particle fluid systems.     

水下目标亮点拓扑特征提取及自动识别方法

针对水下目标逆合成孔径声呐(Inverse Synthetic Aperture Sonar,ISAS)图像识别问题中观测角度随机多变,目标结构相互遮挡问题,提出一种基于多亮点拓扑矢量特征的ISAS水下目标识别方法。通过分析ISAS成像过程中散射点位置由三维空间向二维成像平面的投影关系,表明了横向定标后的声呐图像中强亮点之间的距离仅由目标散射结构之间的物理距离决定,据此基于强亮点之间的相互距离,构造能稳定描述不同观测角度下目标的拓扑矢量特征。然后通过K-means聚类获取多聚类中心以克服目标结构互相遮挡造成的亮点缺失问题。最终采用最近邻分类器实现目标识别。水池缩比模型实验表明,该方法对于水下目标的识别率达到84.0%。