伪彩色数字高程模型在工业CT断层图像检查中的应用

在工业CT断层图像缺陷检测应用中，提出了将数字高程模型DEM与伪彩色技术一起应用于二维灰度图像缺陷检测领域的算法流程。应用该算法流程，能够充分利用CT断层灰度图像信息，获得清晰的视觉检查效果，从而大大增强了系统检测缺陷的能力。在大型工业CT图像重建与检查分系统中采用该算法流程已经取得了较为理想的效果。     

基于非锐化掩模的实时自适应图像增强算法

针对传统的非锐化掩模算法的局限性,依据图像中各像素点及以其为中心的若干相邻像素点的均方差值,提出了自适应图像增强算法的原理,并分析其硬件实现方法,然后给出了该算法与其它算法应用于图像增强的对比结果,最后在FPGA（field programmable gate array）实验板上进行验证.实验结果表明,此算法有效的增强图像的细节区域,防止图像边缘区域出现过冲现象,抑制图像平坦区域的噪声放大.因此,该算法取得了良好的视觉效果,硬件实现简单,适合于实时条件下图像的增强.     

Extending the color gamut and dynamic range of an sRGB image using a residual image

Image sources, such as digital camera captures and photographic negatives, typically have more information than can be reproduced on a photographic print or a video display. The information that is lost during the tone/color rendering process relates to both the extended dynamic range and color gamut of the original scene. In conventional photographic systems, most of this additional information is archived on the photographic negative and can be accessed by adjusting the way the negative is printed. However, most digital imaging systems have traditionally archived only a rendered video RGB image. As a result, it is not possible to make the same sorts of image manipulations that historically have been possible with conventional photographic systems. This suggests that there would be an advantage to storing images using an extended dynamic range/color gamut color encoding. However, because of file compatibility issues, digital imaging systems that store images using color encoding other than a standard video RGB representation (e.g., sRGB) would be significantly disadvantaged in the marketplace. In this article, we describe a solution that has been developed to maintain compatibility with existing file formats and software applications, while simultaneously retaining the extended dynamic range and color gamut information associated with the original scenes. With this approach, the input raw digital camera image or film scan is first transformed to the scene‐referred ERIMM RGB color encoding. Next, a rendered sRGB image is formed in the usual way and stored in a conventional image file (e.g., a standard JPEG file). A residual image representing the difference between the original extended dynamic range image and the final rendered image is formed and stored in the image file using proprietary metadata tags. This provides a mechanism for archiving the extended dynamic range/color gamut information, which is normally discarded during the rendering process, without sacrificing interoperability. Appropriately enabled applications can decode the residual image metadata and use it to reconstruct the ERIMM RGB image, whereas applications that are not aware of the metadata will ignore it and only have access to the sRGB image. The residual image is formed such that it will have negligible pixel values for those portions of the image that lie within the sRGB gamut, and will therefore be highly compressible. Tests on a population of 950 real customer images have demonstrated that the extended dynamic range scene information can be stored with an average file size overhead of about 8% compared to the sRGB images alone. © 2003 Wiley Periodicals, Inc. Col Res Appl, 28, 251–266, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.10160     

Robust图像跟踪：强度—特征混合式目标定位

在像平面上确定目标的位置(由它的瞄准点位置表示)是图像跟踪技术的核心.目前研究的目标是图像定位方法多偏重于单一的工作模式,因而难以适应变化较大的使用环境.本文提出一种旨在综合利用强度相关和特征匹配的混合式目标图像定位方法.假定当前帧图像已经过预处理并完成分割,首先通过一种自适应门限SSDA并辅之以投影排序搜索,尽可能快速地剔除与参考图差别较大的子区;再用一种简便可靠并可具一定记忆能力的特征匹配法,在保留的子区集内,为目标精确定位——宣布瞄准点位置.利用实际序列图像所作实验表明:上述方法较之通常采用的单一工作模式的定位方法,有明显增强的Robust性以及更广的适应性,而增加的计算代价不大.     

人工神经元网络在系统辨识中的应用

本文将人工神经元网络的非线性性和信息的分布性用于非线性静态模型的辨识.对化 工生产中的缩聚反应过程的辨识结果表明,用人工神经元网络来辨识非线性静态模型是可行 的,从而为系统辨识提供了一条新的途径.     

电子认证与授权管理标准体系研究

以国际上在标识、鉴别与授权标准方面已有的工作为基础，提出了我国电子认证与授权管理标准体系框架。     

利用机载SAR图像仿真星载SAR图像

简要介绍了现有的SAR图像仿真方法,提出了一种利用机载SAR图像进行星载SAR图像仿真的方法,对仿真原理进行了阐述,着重研究了图像几何特性、空间分辨率、噪声特性、辐射特性仿真的数学模型及设计流程,最后进行了计算机仿真。结果分析表明得到的仿真图像具有较高的逼真度,该星载SAR图像仿真方法是有效的。     

A digital secure image communication scheme based on the chaotic Chebyshev map

In this paper, a simple image secure communication scheme based on the chaotic Chebyshev map and chaos synchronization is proposed. The scheme relies on the excellent correlation property of chaotic sequences, which is very desirable for secure image communication. The results obtained by computer simulation indicate that the transmitted source image can be correctly and reliably recovered using the proposed scheme, even through a noisy channel. Notably, the scheme possesses relatively high security and can be easily implemented. Moreover, the quality of the recovered image is satisfactory, quantified by such common criteria as the bit‐error rate performance. Copyright © 2004 John Wiley & Sons, Ltd.     

基于数学形态学的红外图像小目标检测

针对红外成像跟踪系统的低信噪比、背景和噪声干扰严重的小目标图像,以及后续的目标识别处理需要目标的灰度信息的特点,设计了一种基于数学形态学的红外灰度图像小目标检测算法,并用FPGA硬件实现。实验表明,数学形态学滤波能够大大提高目标的信噪比,去除背景和噪声干扰,保留目标的灰度信息,满足系统实时性要求,是一种有效的红外灰度图像小目标检测算法。     

一种空间自适应小波门限去噪算法

提出了一种空间自适应小波门限去噪算法,该算法在小波域对含噪小波系数做两次自适应去噪,两次自适应门限分别基于最大似然(ML)方差估计和最大后验概率(MAP)方差估计.仿真结果表明,该算法与其它自适应门限去噪算法相比,去噪后的图象具有更高的峰值信噪比(PSNR).