面阵CCD航空相机斜视图像的几何校正

为增大机载光电侦察系统的视场角,航空相机多采用扫描方式对地摄影成像,文章针对面阵CCD航空相机斜视摄影引起的图像变形进行校正。由于该类相机与载机之间存在方位和俯仰两轴运动,文章同时考虑相机相对载机的旋转角度和载机相对地面的姿态角,建立了更符合该类相机工作状态的六姿态角投影校正模型,推导出同一地物在畸变图像和标准图像上的像素坐标变换关系,采用双线性插值算法对坐标变换后的像素灰度值进行重采样,对航空图像进行自动校正。最后,对某机场的航拍图像进行校正实验,并与基于地面控制点的多项式校正方法以及基于畸变图像和参考图像配准的校正方法进行比较。实验结果表明,六姿态角投影校正模型能够取得比较高的校正精度(可达像素级),当姿态角的测量精度为3′,图像大小为512pixel×512pixel时,图像校正的均方根误差为1.174 7pixel。该方法不需要提供参考图像和野外采集地面控制点数据,便于工程实现,基本满足航空图像处理的稳定可靠、精度高、实时性强等要求。     

Video image mosaic implement based on planar-mirror-based catadioptric system

Planar-mirror-based catadioptric method is one of the most hot topics in recent years. To overcome the disadvantages of the planar-mirror-based catadioptric panoramic camera, described by Nalwa (1996, 2001, 2000), such as the requirement for high-precision optical device designing and the stitching lines in the resulting images, we proposed a planar-mirror-based video image mosaic system with high precision for designing. Firstly, we designed a screw nut on our system, which can be adjusted to locate the viewpoints of the cameras’ mirror images at a single point approximately. It provides a method for those who have difficulties in their designing and manufacturing for high precision. Then, after the image distortion correction and cylinder projection transforms, we can stitch the images to get a wide field of view image by template matching algorithm. Finally, nonlinear weighting fusion is adopted to eliminate the stitching line effectively. The experimental results show that our system has good characteristics such as video rate capture, high resolution, no stitching line and without affection by the depth of field of view.     

嵌入式下公共场所暴力行为视觉检测方法的研究与应用

根据暴力行为发生时往往存在肢体冲突,并伴随着身体全身或局部部位出现较剧烈运动的特点,本文提出了一种基于运动图像的公共场所暴力行为自动识别方法,并能够移植到嵌入式设备中以便于分布式智能监控。首先,采用Lucas-Kanade(LK)光流法获得相邻两帧监控图像间的光流场,通过光流分析法确定各个运动目标的运动状态,并提取它们的光流特征值、光流速度和方向;其次,对各个运动目标的光流特征值、光流速度与方向进行统计分析,以掌握各个运动目标的运动趋势;最后,结合光流场变化情况及运动目标聚集状态判断是否存在暴力行为。实验结果表明,该嵌入式系统识别准确性好、实时性强、可靠性高。     

一种实用广角成像系统几何畸变数字实时校正方法

图像的几何畸变广泛存在于应用广角镜头的成像系统，畸变的存在尤其不利于基于图像分析的定量分析领域。针对一个基于图像分析的红外角位置测量系统，提出一种实用广角成像系统几何畸变数字实时校正方法，阐述其畸变参数的标定方法，图像的空间变换和灰度插值方法，基于实时性要求，给出畸变校正的算法步骤，实验表明，此广角成像系统几何畸变数字实时校正方法有效提高了角位置测量系统的测量精度，并具有一定的实时性能。     

无控制点条件下的PHI影像几何校正

利用空间位置测量系统对因遥感平台不稳定而引起的影像畸变进行校正是目前航空摄影测量常用的方法.介绍了对推帚成像模式在无地面控制点情况下采集的影像进行几何校正的原理和方法,即利用POS数据解算外方位元素并建立共线方程,从而实现影像的几何校正.用PHI航空遥感影像进行了实验.实验表明,在无地面控制点的情况下,利用POS数据辅...     

流场测速中基于深度卷积神经网络的光学畸变校正技术

基于光学成像的流场测量技术,如粒子图像测速技术（PIV）,易受到因流体中折射率的不均匀性或晃动的介质边界引起的光学畸变而带来的影响。这些畸变会使得示踪粒子在图像上的位置分布产生误差且严重影响图像清晰度,从而增大流场速度测量的误差。为了提高光学流场速度测量的测量精度,自适应光学系统可以应用于其中去校正光学畸变。基于图像流场测量中的光学像差具有频率高,动态范围大,空间分辨率高等特点,对于这一应用场景,基于波前校正器件的自适应光学系统受到了器件本身性能的影响。基于深度学习的自适应光学技术在流场测量中的应用,建立了一种基于深度神经网络的无波前校正器件自适应光学校正技术,以深度神经网络代替传统的波前校正器件,用于粒子图像测速技术中的光学畸变校正。为了生成神经网络所需要的训练和测试数据集,搭建了可以实现波前测量的粒子图像测速实验平台,分析并建立了光学畸变在粒子图像上的图像退化模型。最后,以校正后PIV图像的校正效果和流场速度测量结果作为评价标准,对所建立神经网络的畸变校正性能进行了分析。     

一种复杂背景下运动小目标的检测方法

本文将小目标的帧间信息和光流法紧密联系起来,把小目标的检测分为小目标的预处理、帧间差分和使用金字塔迭代Lucas-Kanade的光流法确定目标三个步骤进行．实验结果表明,该方法能够有效地检测运动小目标．     

畸变的衍射机理及其计算与校正研究

从基于点源圆孔衍射的成像原理出发得到了仅有畸变像点的光场分布的计算式,分析探讨了畸变的衍射机理及其计算与特征以及校正方法,模拟实验表明了这一理论和方法的可行性,有助于光学设计中对畸变进行像差平衡与校正和光学加工中对畸变的检测与修正以及数码成像系统中对畸变做电子校正等。     

基于失真图像特性测量的扫描图像校正技术

提出一种扫描图像校正技术，解决书籍扫描时图像背景灰度不匀、几何畸变和局部模糊的问题。该方法不依赖扫描设备的物理参数，完全根据对失真图像的测量来计算校正因子。选取适当的采样窗1：3，基于直方图分析导出背景灰度因子，通过内插校正几何失真，最后进行空变高通滤波以减轻字迹模糊。通过建立背景灰度因子和缩小系数的关系实现对扫描设备的标定，从而在校正同一设备输出的其它图像时可根据背景灰度的变化而不必再检测页面边缘。与原始扫描图像相比，处理后的图像质量大为改善，且光学字符识别（OCR）处理的识别率达到82．6％。     

A new method for distortion correction of electronic endoscope images

A new method to correct the barrel distortion of an electronic endoscope image is presented. A correction model assuming circularly symmetric distortion is introduced with the following model parameters: the center of distortion and the coefficients of polynomials representing the distortion correction in the radial direction. If the imaging system is distortion-free, straight lines in the object space should be imaged as straight lines. Based on this criterion, a distorted image of a standard pattern consisting of a grid of several straight lines is recorded, and the model parameters are then estimated as a basis for straightening distorted lines. This method has the advantage of not needing a careful placement of the standard pattern for calibration. Correction results are presented for the grid pattern chart to verify a sufficient degree of correction. Examples of distortion correction of real intestinal images and physicians' comments are also presented.     

红外搜索跟踪系统的虚警率降低方法

红外全景搜索跟踪系统能对360?大视场提供实时多目标搜索与定位功能,与此同时大视场的引入也带来虚假目标增多,以及同一运动目标在前后生成的两幅全景图像中位置差异过大的困扰.本文针对以上问题,引入图像金字塔变换降低全景图像分辨率,再利用Lucas-Kanade(LK)光流法计算所有疑似运动区域的光流,最后分析所有光流特征结...     

超高速分幅相机中的无鬼像光学系统

超高速光电摄影系统是目前最常用的一种高速摄影设备。在进行超高速光电分幅相机光学系统的设计时,由于面数较多、结构较为复杂,很容易在像面附近形成鬼像,从而影响成像的锐度和对比度,干扰目标的识别。为了解决鬼像的问题,本研究从光学系统中的关键器件像增强器出发,分析了鬼像形成原因,并进一步采用光纤面板对像增强器进行了优化设计,从而克服了鬼像问题。采用本光学系统组成的超高速光电分幅相机拍摄了高压火花放电的过程,实验结果表明,得到的图像无鬼像。利用该系统可以方便地进行纳秒时间尺度的超高速摄影实验研究,拍摄的照片无鬼像,有利于目标的精确识别和解读。     

基于小波的稳健光流计算方法

针对系统误差导致光流计算稳健性较差及精度较低的问题,该文提出一种基于小波多分辨理论的稳健光流计算方法。所提算法基于小波多尺度分辨率特性,将光照条件变化及传感器噪声引起的系统误差包含进光流计算中以改善光流计算的稳健性及估计精度,并通过总体最小二乘法求解超定小波光流方程组以获得光流矢量。仿真结果表明,与传统的Lucas-Kanade算法、Horn-Schunck算法及基于小波的全向图像光流估计方法相比,所提算法可显著改善光流估计精度及稳健性。     

全景视频摄像机的非参数化几何校正与拼接

提出了一种新的多相机全景视频拼接几何校正拼接方法。先用全景视频摄像机组采集标准标定场的稀疏特征点阵,利用自适应网格细分技术把采集的图像和标定场坐标细分到像素级对应,则全景视频的几何校正和拼接可以通过直接映射得到。在摄像机阵列相对位置固定时,可以一次校正多次使用。本文方法避免了普通拼接方法的特征点检测、匹配优化等参数求解过程,尤其是采用具有较大畸变的广角镜头和相邻相机视场重叠较少时更能体现出其优越性,并可以很容易地迁移到类似的应用中。通过一个原型系统,验证了本文方法的有效性。     

Iterative colour correction of multicamera systems using corresponding feature points

Multiview images captured by multicamera systems are generally not uniform in colour domain. In this paper, we propose a novel colour correction method of multicamera systems that can (i) be applied to not only dense multicamera system, but also sparse multicamera configuration and (ii) obtain an average colour pattern among all cameras. Our proposed colour correction method starts from any camera on the array sequentially, following a certain path, for pairs of cameras, until it reaches the starting point and triggers several iterations. The iteration stops when the correction applied to the images becomes small enough. We propose to calculate the colour correction transformation based on energy minimisation using a dynamic programming of a nonlinearly weighted Gaussian-based kernel density function of geometrically corresponding feature points, obtained by the modified scale invariant feature transformation (SIFT) method, from several time instances and their Gaussian-filtered images. This approach guarantees the convergence of the iteration procedure without any visible colour distortion. The colour correction is done for each colour channel independently. The process is entirely automatic, after estimation of the parameters through the algorithm. Experimental results show that the proposed iteration-based algorithm can colour-correct the dense/sparse multicamera system. The correction is always converged with average colour intensity among viewpoint, and out-performs the conventional method.     

HD-YOLO: Using radius-aware loss function for head detection in top-view fisheye images

People detection is commonly used in computer vision systems, particularly for video surveillance and passenger flow statistics. Unlike standard cameras, fisheye cameras offer a large field of view and reduce occlusions when mounted overhead. However, due to the orientation variation of people in fisheye images, head detection models suffer from severe distortion when applied to fisheye images captured by top-view fisheye cameras. This work develops an end-to-end head detection method named HD-YOLO against complex situations in top-view fisheye images. The radius-aware loss function is designed to make HD-YOLO adapt to the impact of fisheye distortion, and the channel attention module is added to the model. We have also created new fisheye-image datasets for evaluation. Experiments showed that HD-YOLO outperforms other baseline methods on public and self-built datasets.     

空间高分辨率光学系统几何畸变标定技术

遥感相机光学系统畸变系数作为影响相机在轨成像质量的关键因素,其检测精度一直以来都是遥感相机研制过程中的核心环节。传统的测角法主要依靠高精度二维转台,实现了光学系统视场角与像高之间的精准对应,该方法对测试设备和测试环境要求苛刻。随着相机焦距、口径和体积的增大,对于转台设备的尺寸与测量精度也日渐提升,单纯依靠提升测试设备性能无法满足后续各类高性能遥感相机的研制需求,尤其对于垂直装调类超大口径空间高分辨率光学系统,该方法不可行。在传统精密测角法的基础上,提出一种基于干涉原理的空间高分辨率光学系统几何畸变标定技术,相比于传统的精密测角法,该方法在同等测试精度的基础上,具备更广泛的适用性,其不再受限于测试设备的尺寸与精度限制,可同时满足各种类型遥感相机的畸变测试需求。文中详细介绍了该畸变测试方的基本原理、测试方法与误差链路,并对该畸变测试方法进行了应用验证,将结果与传统畸变测试方法进行对照,表明该方法的测试精度满足遥感相机的研制要求且适用范围更广,对航天长焦距大口径遥感相机研制及畸变测试有参考借鉴意义。     

An improved Gamma correction model for image dehazing in a multi-exposure fusion framework

A usual problem encountered during bad weather conditions is the degraded image quality due to haze/fog. In basic Gamma correction method there is always an uncertainty regarding the choice of a particular exponential factor, which improves the quality of the input image because of the nonlinearity involved in the process. This issue has been solved in this study by proposing a modified Gamma correction method, in which the exponential correction factor is varied incrementally to generate images. We also propose the implementation of an automatic image selection criterion for fusion which helps chose images with varied and distinct features. The implementation of the multi-exposure fusion framework is done in the hue-saturation-value color space which has close resemblance with the human vision. The intensity channel of the selected images is fused in the gradient domain which captures minute details and takes an edge as compared to other conventional fusion based methods. The fused saturation channel is obtained by averaging fusion followed by enhancement using a non-linear sigmoid function. The hue channel of the input hazy image is left unprocessed to avoid color distortion. The experimental analysis demonstrates that the proposed method outperforms most of the single image dehazing methods.     

Unsupervised fisheye image correction through bidirectional loss with geometric prior

Neural network based methods for fisheye distortion correction are effective and increasingly popular, although training network require a high amount of labeled data. In this paper, we propose an unsupervised fisheye correction network to address the aforementioned issue. During the training process, the predicted parameters are employed to correct strong distortion that exists in the fisheye image and synthesize the corresponding distortion using the original distortion-free image. Thus, the network is constrained with bidirectional loss to obtain more accurate distortion parameters. We calculate the two losses at the image level as opposed to directly minimizing the difference between the predicted and ground truth of distortion parameters. Additionally, we leverage the geometric prior that the distortion distribution depends on the geometric regions of fisheye images and the straight line should be straight in the corrected images. The network focuses more on the geometric prior regions as opposed to equally perceiving the whole image without any attention mechanisms. To generate more appealing corrected results in visual appearance, we introduce a coarse-to-fine inpainting network to fill the hole regions caused by the irreversible mapping function using distortion parameters. Each module of the proposed network is differentiable, and thus the entire framework is completely end-to-end. When compared with the previous supervised methods, our method is more flexible and shows better practical applications for distortion rectification. The experiment results demonstrate that our proposed method outperforms state-of-the-art methods on the correction performance without any labeled distortion parameters.     

基于改进深度学习自编码的图像边沿畸变校正算法研究

图像边沿畸变校正中直线投影衍生边沿拟合度差,导致校正误差大,提出基于改进深度学习自编码的图像边沿畸变校正算法。采用自适应阈值小波去噪算法,对各级尺度参数实施自适应变换,完成噪声去除。根据图像去噪结果,使用费舍尔向量编码优化深度学习结果,提取图像的边沿畸变形态。并以边沿畸变形态提取结果为基础,获取校正目标优化函数,分析边沿断裂情况,实现直线投影衍生边沿拟合;通过确定图像边沿误差评价函数,判断图像边沿畸变校正方式,达到图像边沿畸变校正的目的。以含噪桶形畸变与枕形畸变图像为研究对象进行实验分析,结果表明,所提算法可有效校正图像的桶形畸变与枕形畸变,桶形、枕形图像边沿畸变校正后,图像中的边沿条数和原图一致均为5条,实现高精度、高效率的图像边沿畸变校正。