全景图像海天线提取及舰船目标自动检测

为实现海洋环境下全景摄像机自动目标检测,提出了一种全景图像海天线提取算法及海天线上舰船目标检测方法;首先,分析了全景图像中海天线的成像特点,使用基于分区的自适应阈值Canny边缘检测算法进行边缘检测,并对海天线边缘进行双阈值梯度方向过滤;然后,对图像边缘进行细化,提出了基于最长曲线法的海天线边缘提取算法并进行椭圆拟合得到海天线边缘成像椭圆方程;最后,介绍了根据海天线椭圆方程对海天线上舰船目标进行检测的方法;使用3种不同海洋环境下拍摄的图像进行了实际测试,实验结果表明:该方法可有效地检测出复杂海天背景下的海天线及海天线上舰船目标,海天线提取成功率可达95％以上,对径向成像高度超过10个像素的目标识别成功率可达90％。     

舰船目标红外图像特性研究

针对成像型反舰导弹所获红外图像的特点,分析了舰船目标和海天背景的红外辐射特性,得知舰船目标红外图像有一重要特征-海天线,它可以确定目标的所在区域。为了舰船目标的跟踪定位,对舰船目标红外图像中的海天线特征进行研究,提出一种基于预处理和直线拟合法提取海天线特征的算法,首先对图像进行边缘检测得到梯度图像,进而二值化并细化,最后利用Hough变换提取海天线。结果表明,在低对比度和有云团干扰的情况下,该算法与改进的直线拟合法相比,能够有效地、准确地提取舰船目标红外图像中海天线特征。     

一种复杂海天背景下红外弱小目标检测算法

红外弱小目标的检测是红外搜索跟踪系统(IRST)中的一项关键技术,常用的目标检测算法存在受海杂波严重、虚警率较高等问题,分析了海天背景下红外图像的背景、小目标的特征,提出了一种海杂波背景下的红外小目标检测算法。首先统计图像的行均值和梯度,用最小二乘法拟合出海天线,然后利用形态学算子抑制图像背景,并采用自适应阈值将图像二值化,最后分析图像的梯度特征,抑制海杂波和云层的干扰。实验结果表明,该方法能精确地提取海天线,稳定地提取海天背景下的弱小目标,虚警率低于5%,目标检测概率超过97%。     

舰船小目标图像配准算法

当舰船目标距离红外/可见光复合导引头较远的时候,红外与可见光图像中的目标信息微弱,可供提取的特征较少且差异较大,传统的图像配准算法很难适用.针对该问题,本文提出一种基于传感器参量的图像配准算法,首先根据红外与可见光传感器的成像模型将图像配准分解简化为视场配准与平移配准两个相对分离的步骤;然后利用传感器参量进行图像的视场配准;最后基于海天线和水平高通能量分布确定匹配点完成平移配准.仿真实验结果表明,该算法具有较高的配准准确度,可以应用于实际远距离海上舰船的红外和可见光图像配准.     

船载摄像系统的一种电子稳像算法

提出了一种新的船载摄像系统的电子稳像算法———水天线稳像算法。算法的模型是基于船载摄像机运动和视频图像运动关系建立的。介绍了水天线的特点以及水天线稳像算法的实现,并利用水天线稳像算法对船载摄像系统获取的视频图像序列进行了稳像处理。实验结果表明,该算法具有稳像精度高、实时处理能力强的优点,实际应用效果较好。     

基于灰度特性的海天背景小目标检测

针对复杂海天背景下的小目标检测存在海浪、云层干扰等问题,提出了先提取海天线,然后利用一维最大熵阈值分割法对出现在天空、海面或者海天线附近特定区域的目标进行检测的算法。该算法主要利用天空海面行灰度均值特性,结合梯度运算和形态学运算在海天线的潜在位置中检测边缘,进而用强鲁棒性的Hough变换直线检测法拟合海天线,实现对海天线的准确定位。实验处理分辨率为：256pixel×256pixel的位图时,定位海天线需时4．1ms,检测到目标需时5．3ms,完全满足高帧频图像处理的实时性要求。实验结果表明,该算法能够快速、准确地检测出小目标,大大降低了虚警率。     

一种基于特征不变描述的图像无缝拼接算法

针对尺度变化不明显场景的拼接,提出一种基于特征不变描述的图像无缝拼接算法.利用Harris特征检测算子进行特征点提取,在此基础上对提取出的特征点采用SIFT描述子进行处理,使其具备旋转不变性,然后利用k-d树算法进行遍历搜索定位,并用RANSAC算法进行图像间单应性变换矩阵计算,最后利用加权平均的融合方法进行图像无缝平滑,得到无缝拼接图像.实验结果表明:该算法在图像任意角度旋转的情况下,能有效地实现图像无缝拼接,并具有较强的鲁棒性.     

基于边缘相位编码的复杂背景下海天线检测

复杂的海空背景环境中云层、波浪反射、复杂气象条件等会给海天线检测带来困难。针对复杂背景下海天线检测的环境适应性问题,提出了一种基于图像边缘相位编码的海天线检测方法。该算法通过双边滤波原理对图像进行保边滤波处理,以高斯拉普拉斯算子增强边缘响应,配合设计的高斯核方向滤波器完成边缘相位编码的抑噪和增强,然后以相位组分内扫描线的累积响应强度和海天区域内像素强度分布模式差值辨识出最优海天线的位置。实验结果表明,该算法能够有效地检测出不同复杂背景条件下的海天线,具有计算量小和环境适应性强的特点。     

基于改进主动轮廓模型的全景海天线检测

提出了一种基于改进主动轮廓模型的海天线检测算法,检测全景海域图像中的椭圆海天线。通过全分辨率算法获取图像的视觉显著图,提取全景设备区在全景图像中的位置,以消除全景设备区干扰对海天线检测的不良影响。根据全景海天线为椭圆形这一特征,将构造的形状能量约束项加入主动轮廓模型中作为新的能量函数,使活动轮廓线的形状在收敛过程中受到限制,从而成功收敛到全景海天线上。实验结果表明,该算法对不同拍摄条件下的全景图像均适用,检测准确率达到96%,普适性和稳健性良好。     

高光谱图像中基于端元提取的小目标检测算法

针对高光谱图像中小目标检测问题,提出了一种基于端元提取的目标检测算法。该算法利用主成分分析的变换矩阵来构造投影算子,把原始图像投影到该算子构成的正交子空间后,大概率的背景信息得到抑制,从而突出了小概率的目标;在完成背景信息抑制的基础上,利用迭代误差分析方法进行端元的自动提取;根据所提取出的目标端元的光谱,结合光谱角度匹配技术完成目标物的检测。为了验证新方法的有效性,利用高光谱数据进行了实验研究,并与经典的RX算法的检测结果相比较。实验结果表明提出的基于端元提取的算法不需要目标的任何先验知识就能达到比较好的目标探测效果,对RX算法检测效果不太理想的小目标也能准确识别。     

基于双阈值Huber范数估计的图像正则化超分辨率算法

多帧图像超分辨率算法利用图像间的互补信息, 可以从一系列具有亚像素位移的低分辨率影像数据中重建出高分辨率图像. 在众多超分辨率算法中, 正则化方法以其求解病态问题的有效性而被广泛应用, 但在此类方法中, 最优估计算子的估计准确度对最后的重建结果有着较大的影响. 本文在现有正则化超分辨率重建算法的基础上, 提出了一种基于双阈值Huber范数的极大似然估计算子, 可以提高Huber范数对于阈值取值的容忍性和算子估计精度; 并给出了基于该算子的正则化超分辨率算法的迭代公式. 通过对仿真图像进行重建, 结果表明算法可有效地抑制各种噪声并保证重建效果; 同时将此算法应用于实际图像的超分辨率重建, 有效地提高了目标影像的空间分辨率. 关键词： 图像处理 图像重建 超分辨率 正则化     

Development of High Performance Quantum Image Algorithm on Constrained Least Squares Filtering Computation

Recent development of computer technology may lead to the quantum image algorithms becoming a hotspot. Quantum information and computation give some advantages to our quantum image algorithms, which deal with the limited problems that cannot be solved by the original classical image algorithm. Image processing cry out for applications of quantum image. Most works on quantum images are theoretical or sometimes even unpolished, although real-world experiments in quantum computer have begun and are multiplying. However, just as the development of computer technology helped to drive the Technology Revolution, a new quantum image algorithm on constrained least squares filtering computation was proposed from quantum mechanics, quantum information, and extremely powerful computer. A quantum image representation model is introduced to construct an image model, which is then used for image processing. Prior knowledge is employed in order to reconstruct or estimate the point spread function, and a non-degenerate estimate is obtained based on the opposite processing. The fuzzy function against noises is solved using the optimal measure of smoothness. On the constraint condition, determine the minimum criterion function and estimate the original image function. For some motion blurs and some kinds of noise pollutions, such as Gaussian noises, the proposed algorithm is able to yield better recovery results. Additionally, it should be noted that, when there is a noise attack with very low noise intensity, the model based on the constrained least squares filtering can still deliver good recovery results, with strong robustness. Subsequently, discuss the simulation analysis of the complexity of implementing quantum circuits and image filtering, and demonstrate that the algorithm has a good effect on fuzzy recovery, when the noise density is small.     

Modified Gerchberg–Saxton (G-S) Algorithm and Its Application

The Gerchberg–Saxton (G-S) algorithm is a phase retrieval algorithm that is widely used in beam shaping and optical information processing. However, the G-S algorithm has difficulty obtaining the exact solution after iterating, and an approximate solution is often obtained. In this paper, we propose a series of modified G-S algorithms based on the Fresnel transform domain, including the single-phase retrieval (SPR) algorithm, the double-phase retrieval (DPR) algorithm, and the multiple-phase retrieval (MPR) algorithm. The analysis results show that the convergence of the SPR algorithm is better than that of the G-S algorithm, but the exact solution is not obtained. The DPR and MPR algorithms have good convergence and can obtain exact solutions; that is, the information is recovered losslessly. We discuss the security advantages and verification reliability of the proposed algorithms in image encryption. A multiple-image encryption scheme is proposed, in which n plaintexts can be recovered from n ciphertexts, which greatly improves the efficiency of the system. Finally, the proposed algorithms are compared with the current phase retrieval algorithms, and future applications are discussed. We hope that our research can provide new ideas for the application of the G-S algorithm.     

红外气动退化图像复原校正的复合算法研究

鉴于气动效应图像退化因素的复杂性和随机性,单一复原算法难以满足要求,提出构造复合算法来提高湍流退化图像复原质量的思路,综合各种基于不同理论模型和应用对象的实用算法,实现优势互补。给出了现有算法结合的主要途径,主要探讨了基于统计理论的MAP复原算法与基于正则化理论的总变分算法复合后各算法结构的自适应调整和改造以及各种参数的自适应性选择问题。充分利用各算法的优势,完成基于算法结构相对稳定的智能化组合。在微机上进行了一系列的复原对比实验。实验结果表明：复合算法提高了图像的总体复原质量。     

含冲击波流场干涉图的图像处理方法研究

本文研究了含冲击波流场干涉图的图像处理方法,它包括干涉条纹的快束细化算法,冲击波波阵面的提取算法和时间序列冲击波波阵面的配准算法.根据这些算法,在PC-VISION 100图像处理机上开发了应用软件.实验结果表明,该方法可用于定量计算冲击波的传播速度和压力分布,增加了流场干涉图的用途.     

基于YOLOv3框架的高分辨电镜图像原子峰位置检测

高分辨电镜图像中原子峰位置的检测具有十分重要的现实意义,通过精确定量化原子峰位置可以分析物质在微观尺度上的结构形变、电极化矢量分布等重要信息.近年来深度学习技术在图像目标检测领域取得了巨大突破,这一技术可用在高分辨电镜图像处理上,因为原子位置的检测可以看作是一个目标检测问题.本文利用先进的机器学习方法,通过制作高质量原子图像样本集,使用YOLOv3目标识别框架对原子图像进行自动检测,达到预期效果,实现了深度学习技术在高分辨电镜图像处理领域的应用.该方法的运用有望突破自动处理动态、大量电镜图片的瓶颈问题.     

An evaluation method based on absolute difference to validate the performance of SBNUC algorithms

Scene-based non-uniformity correction (SBNUC) algorithms are an important part of infrared image processing; however, SBNUC algorithms usually cause two defects: (1) ghosting artifacts and (2) over-correction. In this paper, we use the absolute difference based on guided image filter (AD-GF) method to validate the performance of SBNUC algorithms. We obtain a self-separation source using the improved guided image filter to process the input image, and use the self-separation source to obtain the space-high-frequency parts of the input image and the corrected image. Finally, we use the absolute difference between the two space-high-frequency parts as the evaluation result. Based on experimental results, the AD-GF method has better robustness and can validate the performance of SBNUC algorithms even if ghosting artifacts or over-correction occur. Also the AD-GF method can measure how SBNUC algorithms perform in the time domain, it’s an effective evaluation method for SBNUC algorithm.     

A Bit Shift Image Encryption Algorithm Based on Double Chaotic Systems

A chaotic system refers to a deterministic system with seemingly random irregular motion, and its behavior is uncertain, unrepeatable, and unpredictable. In recent years, researchers have proposed various image encryption schemes based on a single low-dimensional or high-dimensional chaotic system, but many algorithms have problems such as low security. Therefore, designing a good chaotic system and encryption scheme is very important for encryption algorithms. This paper constructs a new double chaotic system based on tent mapping and logistic mapping. In order to verify the practicability and feasibility of the new chaotic system, a displacement image encryption algorithm based on the new chaotic system was subsequently proposed. This paper proposes a displacement image encryption algorithm based on the new chaotic system. The algorithm uses an improved new nonlinear feedback function to generate two random sequences, one of which is used to generate the index sequence, the other is used to generate the encryption matrix, and the index sequence is used to control the generation of the encryption matrix required for encryption. Then, the encryption matrix and the scrambling matrix are XORed to obtain the first encryption image. Finally, a bit-shift encryption method is adopted to prevent the harm caused by key leakage and to improve the security of the algorithm. Numerical experiments show that the key space of the algorithm is not only large, but also the key sensitivity is relatively high, and it has good resistance to various attacks. The analysis shows that this algorithm has certain competitive advantages compared with other encryption algorithms.     

基于DSP和FPGA的图像选别系统设计

色选设备是集光、电、气、机及图像处理为一体的高科技综合技术。基于DSP和FPGA的图像选别系统具有精度高、处理速度快、集成度高、功耗小等优点。首先介绍图像选别系统的硬件组成,然后以破损花生检测为研究对象,通过Matlab编程研究不同处理算法的优缺点,最后移植到DSP上测试编写的程序,提出破损花生的检测算法,实现了依据破损面积大小对花生的选别。在花生筛选中,除了破损花生检测,还有花生的大小检测,因此在上述基础上进一步提出花生几何参数的计算方法。     

基于超分辨率重建的图像增强算法研究

 为了提高图像分辨率,从软件的角度出发,对低分辨率图像序列重建高分辨率图像的原理和算法实现开展了研究,提出从多个低分辨率图像序列中获取更高分辨率图像的方法。通过采用基于光流场的金字塔分层结构实现由粗到精的图像配准,获取了亚像素的运动估计。在采用多帧低分辨率图像进行亚像素级配准后,提出采用动态自适应确定正则化参数的方法,构造了简单的正则化代价函数,建立了低分辨率图像与高分辨率图像之间关系的重建模型,仿真实验结果表明该超分辨率重建算法水平和垂直方向上分辨率各增加一倍,与其他算法相比清晰度更高,计算时间不到传统POCS算法的一半。