新的基于边缘特征的图像相关匹配方法

传统的图像相关匹配方法利用求取对应像素灰度差的累加和进行图像之间的相似性度量,或者设定阈值计算具有相近灰度值的像素点的个数进行相似性度量,由于参考图和实测图之间存在着灰度差异、模糊以及局部遮挡等,使得仅依靠图像灰度信息的算法的性能受到影响。结合图像的边界特征匹配技术,提出了一种新的图像相关匹配算法。该算法首先对图像进行边界提取并二值化,然后利用所定义的基于边缘的相似距离(ESD)对处理后的二值图像进行相似性匹配运算。试验结果表明这种算法在多种图像畸变情况下的有效性。     

AN EXPANSION AND CORRECTION ALGORITHM FOR PHOTOELECTRIC IMAGE WITH GEOMETRIC DISTORTION

A creepy hotoelectric endoscopy system with good performance is studied,and an expansion and correction algorithm for a compressed photoelectric image with serious geometric distortion is presented.The algorithm can not only correct the geometric geometric distortion,but also restore the gray-level distribution by means of ternary convolution algorithm.The details and the outline in the image are very clear.It is proved to be of high performance in practice.     

基于小波多尺度表示的图像匹配研究

根据小波变换系数对信号平移步长的变化规律,提出一种基于小波金字塔结构的、遍历式的图像匹配方法,这种方法消除了由于小波变换对平移的敏感性所引起的误匹配,在匹配策略上,以小波分解高频分量的匹配为主。实验证实,本匹配方法对实时图和参考图的局部灰度反转不敏感,具有一定抗几何失真的能力,优于经典的灰度相关匹配。     

基于稀疏表达的图像去噪方法研究

提出一种基于混合字典的图像稀疏分解去噪方法。使用小波包函数和离散余弦函数构成混合字典,采用匹配追踪算法对图像进行稀疏分解,提取含噪图像中的稀疏成分,最后利用稀疏成分进行图像重构,达到去除图像中噪声的目的。实验中与单一字典稀疏分解去噪算法进行了对比,结果表明,所提出的混合字典稀疏去噪算法可有效提取图像中的稀疏结构,改善重构图像的主客观质量。     

Image alignment based perceptual image hash for content authentication

Perceptual image hash is an emerging technology that is closely related to many applications such as image content authentication, image forging detection, image similarity detection, and image retrieval. In this work, we propose an image alignment based perceptual image hash method, and a hash-based image forging detection and tampering localization method. In the proposed method, we introduce an image alignment process to provide a framework for image hash method to tolerate a wide range of geometric distortions. The image hash is generated by utilizing hybrid perceptual features that are extracted from global and local Zernike moments combining with DCT-based statistical features of the image. The proposed method can detect various image forging and compromised image regions. Furthermore, it has broad-spectrum robustness, including tolerating content-preserving manipulations and geometric distortion-resilient. Compared with state-of-the-art schemes, the proposed method provides satisfactory comprehensive performances in content-based image forging detection and tampering localization.     

一种基于PCNN的图像去噪新方法

该文深入研究了如何用一种有生物学依据的人工神经网络脉冲耦合神经网络(PCNNPulseCoupled Neural Network)进行二值图像去噪与图像平滑,并提出了基于PCNN的图像去噪算法。计算机仿真结果表明,使用PCNN可有效地恢复被噪声污染的二值图像,且恢复图像的信噪比增量高于用另两种常用的图像恢复方法(中值滤波与均值滤波)得到的结果。     

一种基于图像插值的SIFT算法研究

SIFT特征是图像的局部特征,其对旋转,尺度缩放,亮度变化保持不变性,对视角变化、一定角度的仿射变换、噪声也保持一定程度的稳定性.提出了一种具有尺度不变特征转换的图像插值的SIFT算法.首先对图像进行SIFT特征提取,利用K-D树搜索并计算最近邻特征向量与次最近邻向量间的欧式距离来实现对特征点的匹配.在此基础上加入图像插值法增大采集特征点的范围,从而增加特征点匹配的对数.最后比较3种图像插值方法.实验结果表明：双线性插值法获取的特征点匹配的对数最多,但匹配时间较长.     

基于主成分分析的图像匹配方法研究

景像匹配和相关跟踪系统中,由于所面临的都是变化的场景,实时获取的图像与预存的基准图之间存在差异。传统的基于图像空间的匹配方法不能很好地克服这些差异给图像匹配定位带来的影响,使得完全按图像灰度特征的相关匹配应用受到限制。文中将主成分分析方法引入了图像匹配和相关跟踪过程中,提出一种能抗御一定图像畸变的基于主成分分析的图像匹配算法,并应用该方法进行了初步实验,证明该算法具有较高的匹配跟踪鲁棒性。     

基于边缘强度的红外图像阈值分割方法研究

提出一种针对复杂场景的基于图像边缘强度的阈值分割算法。首先进行边缘检测,然后根据边缘强度进行像素灰度值加权平均计算出图像的分割阈值。该算法简单、实用,可对海上及空中目标进行准确分割,并已在实际的跟踪系统中进行了应用,结果表明目标分割正确,系统实时性强、稳定可靠。     

基于双平台直方图的红外图像增强算法

针对红外图像特别是红外弱小目标图像的特点,提出了一种基于双平台的直方图均衡算法。在对统计直方图设置两个不同的平台阈值的基础上,对修改后的直方图进行均衡化处理,然后对得到的图像进行灰度间距上的均衡化处理。算法中的上限平台阈值对背景和噪声进行了适当抑制,下限平台阈值对小目标和细节进行了适当放大,从而克服了一般直方图均衡化在对红外图像增强时的缺点。相对单平台直方图的均衡算法,能够增强图像整体效果,同时较好地保持了图像细节。     

Speckle noise removal based on structural convolutional neural networks with feature fusion for medical image

Application of convolutional neural networks (CNNs) for image additive white Gaussian noise (AWGN) removal has attracted considerable attentions with the rapid development of deep learning in recent years. However, the work of image multiplicative speckle noise removal is rarely done. Moreover, most of the existing speckle noise removal algorithms are based on traditional methods with human priori knowledge, which means that the parameters of the algorithms need to be set manually. Nowadays, deep learning methods show clear advantages on image feature extraction. Multiplicative speckle noise is very common in real life images, especially in medical images. In this paper, a novel neural network structure is proposed to recover noisy images with speckle noise. Our proposed method mainly consists of three subnetworks. One network is rough clean image estimate subnetwork. Another is subnetwork of noise estimation. The last one is an information fusion network based on U-Net and several convolutional layers. Different from the existing speckle denoising model based on the statistics of images, the proposed network model can handle speckle denoising of different noise levels with an end-to-end trainable model. Extensive experimental results on several test datasets clearly demonstrate the superior performance of our proposed network over state-of-the-arts in terms of quantitative metrics and visual quality.     

Massive-scale image retrieval based on deep visual feature representation

This paper proposes an image retrieval algorithm towards massive-scale multimedia data. In order to be consistent with human visual system, we first design a color attention function to describe the important of different image patches. Subsequently, we combine color and texture to construct candidate regions, which will be fed into a deep neural network (DNN) for deep representation extraction. Then, we design a similarity function to calculate the distance among different images, where top-ranking images are considered as the required images. Experimental results show the effectiveness and robustness of our proposed method.     

一种基于混合变换的分层图像表示与编码

提出一种基于混合变换的分层图像编码算法，它将原始图像划分成包含不同结构特征的图像平面，如平滑边缘层和纹理层等等，然后采用不同的压缩算法对不同的图像层进行编码，从而达到对整幅图像最优编码的目的。本文实际上给出了一种新的编码框架，在这个框架内综合了不同的信号变换方式，将它们有机结合起来能够提供一种有效的图像表示方式。实验结果表明，与采用单一小波图像编码算法相比，本文算法能够获得更好的图像恢复质量。     

Stitched image quality assessment based on local measurement errors and global statistical properties

Image stitching is developed to generate wide-field images or panoramic images for virtual reality applications. However, the quality assessment of stitched images with respect to various stitching algorithms has been less studied. Effective stitched image quality assessment (SIQA) is advantageous to evaluate the performance of various stitching methods and optimize the design of stitching methods. In this paper, we propose a novel SIQA method by exploiting local measurement errors and global statistical properties for feature extraction. Comprehensive image attributes including ghosting, misalignment, structural distortion, geometric error, chromatic aberrations and blur are considered either locally or globally. The extracted local and global features are aggregated into an overall quality via regression. Experimental results on two benchmark databases demonstrate the superiority of the proposed metric over both the state-of-the-art quality models designed for natural images and stitched images.     

基于低速信道的远程图象监控系统

提出了一种以计算机通信为核心采用图象压缩技术在低速信道（公用电话网，ＤＤＮ）上实现远程图象监控的方案，并进行了系统研制。该系统特别适合于图象的存储与管理，已成功地应用于电话局程控机房的无人值守系统。     

双谱图像立体彩色融合显示

论述了双路非同步视频信号输入情况下的立体显示的实现;分析了双谱图像融合和立体显示两种技术的异同之处;提出了在立体显示过程中实现双谱图像的假彩色融合;并在研究紫外图像和微光图像特性和预处理方法的基础上,讨论了紫外波段和微弱可见光波段双路单色视频图像在立体显示过程中的假彩色融合、双谱图像立体彩色融合显示方法的适用范围,以及该方法的具体改进措施。     

基于特征点的影像匹配

在数字摄影测量中,影像匹配代替了传统的人工观测,来达到自动确定同名像点的目的。影像匹配的理论与实践,是实现自动立体量测的关键,也是数字摄影测量的重要研究课题之一。本文中对基于特征点的影像匹配思路及特征点的提取与匹配中所涉及到的各种算法进行说明,并通过实验对算法展开分析。     

基于红外图像的地面自主车导航的道路边界检测

讨论了用于地面自主车（ALV）视觉导航的红外道路图像的特点,论述了红外道路图像的预处理策略和方法,分析了一些常用的图像处理方法在处理红外道路图像时的缺陷,指出由于温差的渐变性。红外道路图像的路边检测特别适合使用阈值穷举法．并给出了在温度渐变的路边上边缘强度的求取方法和使用二阶微分算子得到边界线段的精确位置的方法。同时使用了边缘信息和灰度信息来提取道路边界信息,阈值穷举法和二阶微分算子得到的道路边界线段相互融合。得到候选的道路边界线段,在ALV的实际应用中取得了很好的效果。     

基于彩色图像的增强算法

本文在分析常用锐化算法的基础上，对其进行改进，提出了一种新的针对彩色图像的边缘增强算法，并用模糊集理论选取阈值去除噪声。实验证明，该算法能有效地突出彩色图像的边缘，与其它算法比较，该算法耗时很少。