拼接显示墙颜色自动校正系统设计

设计了一种拼接显示墙颜色自动校正系统。介绍了拼接显示墙颜色调整的原理和方法;阐述了系统配置和颜色自动调整的实现流程。通过该自动颜色调整系统,可以调整不同显示屏之间的亮度差异和色彩差异,较好地解决了传统拼接显示墙屏体间的色彩差异问题。该自动校正系统也可以大幅提高拼接显示墙颜色校正的精度和效率。     

基于HDR的投影机亮度曲线标定

多台投影机的无缝拼接校正过程主要是投影机的几何校正和颜色校正。多台投影机的颜色校正要求是求出每台投影机的颜色曲线,并对其颜色的相同输出做对应的输入使之达到全局的颜色统一。采用基于相机HDR的方法,先求出相机的亮度响应曲线,再以相机的亮度响应曲线为基础,使相机的输入为投影机的输出,间接求得投影机的亮度响应曲线。实验结果表明,基于HDR的亮度曲线测量,与直接使用照度计比较,结果相差在10%以内。     

近景数码影像中墙面纹理自动拼接方法的研究

影像拼接是从近景数码影像中生成墙面纹理需要解决的问题。然而由于现有的拼接软件通常不适用于处理几何变形大的近景影像,但通过对从近景数码影像中自动拼接墙面纹理的方法进行的研究发现,在相机的位置离建筑物相对较远,且大致沿一条直线进行拍摄时,当原始影像预先纠正到同一个铅垂面上后,由于各影像之间的大小比例接近于1,因此对于此种情况的拼接而言,就只需要通过选取拼接点来确定相邻影像之间沿X轴和Y轴方向的平移量,即可实现拼接。然而对于比较狭窄的街道(如步行街)来说,则由于摄站与建筑物之间的距离比较小,使影像的倾角增大,导致影像的几何变形增大,所以不适于采用选取拼接点的方法进行拼接。针对此种情况的拼接,提出了一种类似于航带法生成正射影像的条带法,用来进行墙面纹理的自动拼接,并且结合影像间同名水平直线的约束和加密匹配同名点两种方法来对拼接结果进行优化。最后针对地面实拍的序列影像进行了实验。实验结果证明,该方法能够显著消除相邻影像间的几何差异,从而真正实现了影像的无缝拼接。     

多模态CCD相机辐射校正方法研究

多模态CCD相机可以根据地物辐亮度的差异,调节相机的积分时间和增益以获取理想的影像数据。如何对不同积分时间和增益设置下获取的数据进行辐射校正,是数据定量化运用的关键。为了因避免相机设置改变而重复辐射定标,本文通过实验发现相机暗电流受积分时间和增益的影响较小,在此基础上本文提出一种辐射校正方法可以基于某种积分时间和增益设置下的辐射校正函数推算其它设置下的辐射校正函数。实验结果显示此方法较为理想。     

基于Kinect的深度数据融合方法

与传统三维激光扫描仪相比, Kinect作为一种新型深度相机, 具有价格低廉、深度数据获取能力强、RGB影像与深度影像同步获取等优势, 然而面对较大室内场景精细建模却存在数据量大、建模范围有限、对硬件环境依赖性强等问题。因此, 在现有单一模型建模基础上, 提出了基于Kinect深度影像的多模型数据融合方法, 实现模型间的自动拼接。最后通过两组实验对提出的数据融合方法进行了验证, 并取得了较好的模型拼接效果。     

一种用于图片来源检测的相机参考模式噪声提取算法

在传统相机参考模式噪声提取算法中,采用多张图片传感器模式噪声的平均值作为相机的参考模式噪声,但其未滤除插值颜色产生的噪声且未考虑不同类型的图片在计算相机参考模式噪声时所占比重,导致提取的相机参考模式噪声效果较差。为此,根据彩色滤波矩阵噪声方差的特点以及图片纹理和亮度的差异,提出一种改进的相机参考模式噪声提取算法。通过滤除插值颜色产生的噪声获取所有图片的真实传感器模式噪声,按照图片纹理和亮度乘积得到每张图片传感器模式噪声在相机参考模式噪声中所占比重,从而计算出相机的参考模式噪声。实验结果表明,与传统相机参考模式噪声提取算法相比,该算法具有更高的图片来源检测准确率。     

无地面控制的无人机影像全自动快速拼接

为了满足地理国情监测、灾害应急保障需要快速响应和服务的应用需求,该文提出一种无地面控制的无人机影像全自动快速拼接方法。该方法利用尺寸不变特征匹配和相关系数匹配获取分布均匀且高精度的像点观测,通过自由网光束法平差获取定向参数和加密点物方坐标,最后通过结合影像的色彩一致性处理和顾及重叠的面Voronoi图接缝线网络快速拼接获取测区影像图。实验结果表明:所提出的方法可有效解决无人机影像的快速拼接问题,获取的影像图具有较高的拼接精度,同时色彩过渡自然。     

一种全景图快速生成算法及其实现

针对柱面全景图拼接提出一种快速稳定的拼接算法。该方法将待拼接的二维图像的灰度值投影变换到一维数轴上，选择特征明显的灰度段作为模板，通过序贯相似性检测进行匹配，并在匹配过程中能够自适应地调整阀值，最后采用线性加权法对图像进行无缝拼接。采用普通相机拍摄的序列图像进行实验，实验结果证明该方法提高了拼接速度，降低了图像亮度差异的影响，使结果图像自然逼真。     

秩亏自由网平差在近景影像色彩均衡处理中的运用

相对于卫星遥感影像和航空影像,近景影像的亮度和色调差异更大,色彩均衡处理困难。本文将秩亏自由网平差应用于近景影像的色彩均衡处理,并通过金字塔影像和并行处理技术提高处理效率。最后,通过给定的评价指标及目视效果验证了该方法在近景影像拼接和三维纹理映射的应用中均可以取得较好的效果。     

自动采集样本的图像颜色传递算法

图像的颜色传递是将一幅图像的颜色特征传递给另一幅图像,使目标图像具有与源图像相似的色彩。提出了一种自动采集样本的图像颜色传递方法,利用无监督学习聚类把目标图像和源图像分别分成一定数量的色彩相近的子块,通过计算各子块的纹理特征和亮度统计值,建立样本块之间的对应关系。从各子块中取出数据点密度较大的像素组成样本块。进而利用最佳匹配算法,查找目标样本块的像素在源图像对应样本块中的最佳匹配像素,将其颜色值传给目标像素。以目标图像中已完成颜色传递的样本块作为参考样本,使用纹理对比方式完成样本块以外其它像素的颜色传递。该自动采集样本算法既可用于彩色图像间颜色传递,也适用于灰度图像与彩色图像间颜色传递。在目标图像和源图像的各子块有明显纹理和亮度特征时,该算法可方便地用于批处理和视频中。     

A flexible vehicle surround view camera system by central-around coordinate mapping model

The surround view camera system is an emerging driving assistant technology that can assist drivers in parking by providing top-down view of surrounding situations. Such a system usually consists of four wide-angle or fish-eye cameras that mounted around the vehicle, and a bird-eye view is synthesized from images of these cameras. Commonly there are two fundamental problems for the surround view synthesis, geometric alignment and image synthesis. Geometric alignment performs fish-eye calibration and computes the image perspective transformation between the bird-eye view and images from the surrounding cameras. Image synthesis technique dedicates to seamless stitch between adjacent views and color balancing. In this paper, we propose a flexible central-around coordinate mapping (CACM) model for vehicle surround view synthesis. The CACM model calculates perspective transformation between a top-view central camera coordinate and the around camera coordinates by a marker point based method. With the transformation matrices, we could generate the pixel point mapping relationship between the bird-eye view and images of the surrounding cameras. After geometric alignment, an image fusion method based on distance weighting is adopted for seamless stitch, and an effective overlapping region brightness optimization method is proposed for color balancing. Both the seamless stitch and color balancing can be easily operated by using two types of weight coefficient under the framework of the CACM model. Experimental results show that the proposed approaches could provide a high-performance surround view camera system.

     

Parallax correction via disparity estimation in a multi-aperture camera

In this paper, an image fusion algorithm is proposed for a multi-aperture camera. Such camera is a feasible alternative to traditional Bayer filter camera in terms of image quality, camera size and camera features. The camera consists of several camera units, each having dedicated optics and color filter. The main challenge of a multi-aperture camera arises from the fact that each camera unit has a slightly different viewpoint. Our image fusion algorithm corrects the parallax error between the sub-images using a disparity map, which is estimated from the single-spectral images. We improve the disparity estimation by combining matching costs over multiple views using trifocal tensors. Images are matched using two alternative matching costs, mutual information and Census transform. We also compare two different disparity estimation methods, graph cuts and semi-global matching. The results show that the overall quality of the fused images is near the reference images.     

子图像加权的彩色图像对比度增强算法

为了更好地实现彩色图像的对比度增强,提出一种有效的基于亮度保持的子图像加权对比度增强算法.首先利用基于亮度保持的双直方图均衡(BBHE)算法定义了2个子图像;然后根据输入图像以及2个子图像的亮度均值,给出一种基于亮度保持的权重系数的计算方法;最后对2个子图像进行加权求和,得到输出图像.对具有不同对比度的彩色图像的实验结果表明,与其他算法相比,文中算法能够有效地实现对比度增强,并且不会出现局部过度增强以及细节丢失等噪声问题,使得输出图像显得更为自然.     

Edge-preserving color image denoising through tensor voting

This paper presents a new method for edge-preserving color image denoising based on the tensor voting framework, a robust perceptual grouping technique used to extract salient information from noisy data. The tensor voting framework is adapted to encode color information through tensors in order to propagate them in a neighborhood by using a specific voting process. This voting process is specifically designed for edge-preserving color image denoising by taking into account perceptual color differences, region uniformity and edginess according to a set of intuitive perceptual criteria. Perceptual color differences are estimated by means of an optimized version of the CIEDE2000 formula, while uniformity and edginess are estimated by means of saliency maps obtained from the tensor voting process. Measurements of removed noise, edge preservation and undesirable introduced artifacts, additionally to visual inspection, show that the proposed method has a better performance than the state-of-the-art image denoising algorithms for images contaminated with CCD camera noise.     

Color Characterization and Balancing by a Nonlinear Line Attractor Network for Image Enhancement

A novel method to map high dynamic range scenes to low dynamic range images utilizing the concept of color characterization, enhancement, and balancing is described in this letter. Each pixel of the image is first characterized by extracting the relationship of the red, green, and blue components along with its corresponding neighbors using a nonlinear line attractor network to form an associative memory. Then, the illumination enhancement process is performed using a hyperbolic tangent function to provide dynamic range compression to each pixel in the image. The slope of the hyperbolic tangent function is controlled using a parameter that is determined by the local and global statistics of the image to facilitate the change of the intensity level. A color balancing process restores the original color characteristics of the image based on learned associative memory matrices which eliminate image distortion due to improper recombination of red, green and blue components after enhancement. Experiments conducted on images captured at extremely uneven lighting environments show that the proposed method outperforms other image enhancement algorithms.     

一类嵌入式图像检测系统的设计与实现

本文设计并实现了针对实验室检测环境的一类嵌入式图像检测系统。该系统基于嵌入式开发平台搭建,通过与开发板连接的USB摄像头采集彩色待检测图像,然后通过分析处理图像的颜色特征来获取检测物的含量等信息。本文针对检测图像的颜色特征和空间结构信息,采用了颜色平衡、特征增强和金字塔分割等图像处理方法。最后,本文以一组液态有色试剂含量检测为例,验证了本图像检测系统的可行性。     

基于小波变换的多聚焦彩色图像融合新方法

给出了一种基于小波变换的多聚焦彩色图像融合方法。首先对待融合图像的R、G、B三分量进行小波变换,将其分成不同频率的子图像。然后对于低频和高频子图像采用不同的算法进行融合,得到融合图像的小波系数。最后利用小波逆变换重构融合图像。实验结果表明,融合图像具有较小的颜色失真和较高的峰值信噪比。     

核密度估计的单幅图像相机响应逆函数求解方法

从数码相机的成像特性出发,详细介绍了通过图像边缘图块色彩的非线性分布来求解其相机响应逆函数的过程,提出了一种基于核密度估计的相机响应逆曲线求解方法。实验证明,该方法能够获得更加准确的相机响应逆函数,用于图像真伪鉴别可以得到较高的识别正确率。     

大幅面拼接航摄仪的两种平台检校方法分析

提出了大幅面拼接航摄仪的两种平台检校方法,并对检校公式进行严密推导。将现有影像按照拼接模型进行拆分,获得大倾角的子影像,由此子影像数据对两种平台检校方法进行验证,根据平台检校结果对检校方法存在的问题和适用性进行了分析。     

Image copy-move forgery passive detection based on improved PCNN and self-selected sub-images

Image forgery detection remains a challenging problem. For the most common copy-move forgery detection, the robustness and accuracy of existing methods can still be further improved. To the best of our knowledge, we are the first to propose an image copy-move forgery passive detection method by combining the improved pulse coupled neural network (PCNN) and the self-selected sub-images. Our method has the following steps: First, contour detection is performed on the input color image, and bounding boxes are drawn to frame the contours to form suspected forgery sub-images. Second, by improving PCNN to perform feature extraction of sub-images, the feature invariance of rotation, scaling, noise adding, and so on can be achieved. Finally, the dual feature matching is used to match the features and locate the forgery regions. What's more, the self-selected sub-images can quickly obtain suspected forgery sub-images and lessen the workload of feature extraction, and the improved PCNN can extract image features with high robustness. Through experiments on the standard image forgery datasets CoMoFoD and CASIA, it is effectively verified that the robustness score and accuracy of proposed method are much higher than the current best method, which is a more efficient image copy-move forgery passive detection method.