基于平台直方图的红外图像自适应增强算法

针对红外图像的特点, 提出了一种基于平台直方图均衡化的自适应红外图像增强算法. 该算法通过自适应地选择平台阈值, 对红外图像进行增强处理, 克服了采用一般直方图均衡化增强红外图像的缺点, 同时算法的运算量远远小于其他平台直方图均衡化算法, 便于实时实现. 理论分析和仿真结果均表明, 该算法对红外图像具有很好的增强效果, 可较好的抑制背景的增强, 突出目标.     

基于动目标检测的夜间视频实时增强技术

为了在普通PC环境中实现夜间视频的实时增强,对Ardely提出的DADPEQU夜间图像增强方法进行了改进,取得了更适合夜间图像增强的算法。设计了基于动目标检测的夜间视频实时增强策略,通过背景模板的使用,大大减少了增强处理的计算量,大幅提高了处理速度。将改进后的算法与增强策略结合,对夜间视频图像实时增强进行了实验,结果证明该算法既能取得较好的增强效果,又能满足实时处理的速度要求。     

图像增强算法综述

图像增强算法能够提高图像整体和局部的对比度,突出图像的细节信息,使增强后的图像更符合人眼的视觉特性且易于机器识别,在军事和民用领域具有广泛的应用。本文从图像增强算法的原理出发,归纳总结了近年来应用比较广泛的4类图像增强算法及其改进算法,包括直方图均衡图像增强算法、小波变换图像增强算法、偏微分方程图像增强算法和基于Retinex理论的图像增强算法。结合人眼视觉特性、噪声抑制、亮度保持和信息熵最大化等图像增强的改进算法,在保证增强图像具有较高对比度的前提下,可进一步提升图像的质量。实现了9种较为典型的图像增强算法,采用主观和客观的评价方法对增强效果进行了对比,分析了不同增强算法的优缺点,并给出了这些算法的计算时间。对这些算法的深入研究能够推动图像增强技术向更高水平发展,从而使图像增强技术在多个学科领域发挥重要作用。     

航天遥感相机星上自适应图像增强研究与实验

为了解决航天遥感图像在地面增强处理带来的滞后性和失真性,因此提出了星上自适应图像增强的方法和一种新的基于自适应线性拉伸的拉普拉斯滤波算法,来实现星上图像增强处理的实时性和自适应性;在拉普拉斯滤波的基础上,用自适应调整参数A和B对图像的对比度和边缘进行增强,该算法结构简单,运算量小;最后建立以FPGA为核心的硬件系统平台,采用流水线的处理方式,对该算法进行实验验证;实验结果表明,与其他增强算法相比,文章算法增强的图像目视效果更好,信息熵提高了10.21%,处理一幅图像所需的时间是79.6 ms,满足航天遥感相机星上自适应图像增强实时性的要求,达到了预期效果。     

一种使用混合智能优化算法的图像增强方法

对比度增强在图像处理系统中常用来提高降质图像质量或增强图像细节。从优化问题的角度来处理图像增强,提出了用混合智能算法,结合细菌觅食优化算法和粒子群优化算法的优点,对图像增强算子的参数进行优化,使图像质量达到最佳。使用的增强算子取决于源图像的局部灰度信息和全局统计信息,采用的适应度函数是基于图像的边缘信息和熵信息。仿真和实验结果表明该方法不仅能实现图像对比度增强还能有效提高目标图像的细节,并有效抑制噪声。     

基于曲率滤波和梯度变换的图像增强

针对基于梯度变换的图像增强算法抗噪声干扰能力差的问题,引入曲率滤波理论,提出了基于高斯曲率滤波和梯度变换的图像增强算法.该算法通过对图像梯度场进行非线性变换来增强图像对比度,通过构造能量泛函,采用梯度下降法从变换后的梯度场重构出增强后的图像,并利用高斯曲率滤波对梯度下降法迭代过程中的重构图像及其各阶偏微分进行平滑,有效解决了图像重构过程中的噪声非线性放大和扩散问题,同时保留了丰富的细节信息.采用多组边缘模糊图像进行仿真实验,实验结果表明该算法在增强图像边缘对比度的同时,能够有效抑制噪声.     

基于局部自适应拉升窗的复合图像增强算法

针对含有低亮度低对比度区域的图像,提出基于局部自适应拉升窗(LASW)的复合图像增强算法.通过研究目前一系列基于局部操作的空域图像增强算法,提出全局和局部操作结合的总体思路;首先使用高提升拉普拉斯(Laplacian)反锐化掩模(UM)增强以获得较多的隐藏细节和边缘信息,然后构造局部自适应拉升窗大幅增强低对比度图像细节,同时使用自适应滤波器进行掩模平滑操作;最后根据局部增强结果进行全局修正.仿真实验表明,在绝对误差、图像熵等评价指标下,该算法使低对比度图像尤其当含有低亮度微弱局部信息时,获得了较好的增强效果.     

基于Contourlet变换和形态学的图像增强方法

传统的图像增强算法在增强图像时,增大噪音,同时丢失细节.针对该问题,结合Contourlet变换中相关系数理论,提出了基于Contourlet变换和数学形态学的图像增强新算法.首先,对图像进行Contourlet变换分解,采用数学形态学算子对高频细节部分区分为细节信息和噪音产生的系数,然后,对变换系数采用非线性映射函数进行增强.最后,利用修改后的变换系数进行Contourlet逆变换得到增强后的图像.实验表明,该方法无论是增强效果还是抗噪性能都明显优于传统的图像增强算法.     

基于迭代直方图均衡化的常规光源下水下成像增强算法

为了增强常规光源下水下图像的视觉对比度,提高水下图像的图像质量,提出一种基于迭代直方图均衡化水下图像增强算法.首先通过Retinex模型将水下图像分解为细节层和光照层图像.然后推导出一个图像增强模型,该模型能够在保证韦伯对比度的前提下完成图像增强工作.接着提出一种基于迭代直方图的直方图均衡化算法对光照层图像进行对比度增强,并通过S形状函数对细节层图像进行对比度拉伸.最后,合并拉伸后的细节层图像和增强后的光照层图像,进而获得较佳的图像增强效果.实验结果表明,该算法能够有效地提升水下图像的视觉对比度,图像信息熵值及均值结构相似度高于其他算法,图像的视觉效果得到显著提高.     

基于DSP阵列的海面目标红外图像实时仿真系统研究

从海面目标红外辐射特性研究的具体需求出发,给出了一种用于生成海面目标红外图像的仿真算法,在此算法基础上提出了一种基于DSP阵列的海面目标红外图像实时仿真系统,并对其系统构成、DSP阵列硬件实现、仿真算法并行处理等内容进行了具体描述。仿真实验结果表明该系统能满足海面目标红外图像实时仿真的基本要求,在同等仿真精度下其仿真实时性远高于其他通用计算平台。     

多子直方图均衡微光图像增强及FPGA实现

针对微光图像对比度低,目标难以识别的问题,对微光图像增强算法进行了研究。提出了一种多子直方图均衡增强算法,该算法首先将直方图按面积平均分割成4个子直方图,利用平均像素数量作为阈值切割直方图降低过度增强现象,然后加入尺度因子对动态范围进行调整,最后分别对子直方图均衡得到增强效果。此算法用Verilog语言在现场可编程门阵列（FPGA）上具体实现,并给出了主观和客观的评价,改进算法能产生更清晰的图像,在硬件平台上也能实时显示增强效果,一帧图像处理时延约为0.45 ms。实验结果表明,改进算法不会产生饱和、噪声放大的现象,图像细节保持较好,满足视频图像处理实时性要求,得到了具有较好视觉效果的增强图像。     

A categorization method of infrared polarization and intensity image fusion algorithm based on the transfer ability of difference features

The fusion of infrared polarization and intensity image can significantly improve the detection performance of target, and the fused image is more suitable for human visual perception and further image-processing tasks. In this paper, a new categorization method of infrared polarization and intensity image fusion algorithm based on the transfer ability of difference feature is proposed. Firstly, the difference feature between two kinds of image and the characteristics of different fusion algorithms are analyzed and summarized. Second, an evaluation vector of fusion algorithm for difference feature transform ability is constructed. Thirdly, the transfer ability of fusion algorithm for difference feature is estimated by the evaluation vector, and the degree of transfer ability of fusion algorithm for difference feature is analyzed. Finally the fusion algorithms are classified by the degree of transfer ability of fusion algorithm for difference feature. The results shows that the proposed fusion algorithm categorization method helps select fusion algorithms in actual scene.     

基于Gadgetron平台的多GPU分布式磁共振图像重建

为了满足磁共振成像（MRI）临床扫描的需求,磁共振图像重建算法的开发一直在不断进行.目前广泛使用的算法实现方式是利用中央处理器（CPU）对磁共振扫描数据进行数学变换得到图像,随着算法复杂度的提升,计算性能问题逐渐显露.利用CPU在大数据量下执行复杂算法时,计算并行性的缺失以及运算中产生的海量数据的存储负荷会导致计算变得极为缓慢,使得一些算法因为重建时间过长,在临床上面临难以推广的问题,也制约了基础研究中新算法的研发.本文设计并实现了一种新的重建算法执行方式,利用Gadgetron磁共振软件重建平台在多核CPU基础上搭载多块图形处理器（GPU）,将磁共振图像重建以分布式并行计算方式实现,并以重建耗时较长的3D径向数据采集Stack of Star（SOS）的图像重建为实例,展示这种重建的实现方法能以相对低廉的硬件成本极大提升重建的速度.     

Rapid Diffusion Weighted Imaging with Enhanced Resolution

In this paper, a new, fast compressively sensed diffusion magnetic resonance image enhancement technique is presented. This algorithm aims to overcome two major obstacles—image resolution limitation and algorithm reconstruction time efficiency-by combining a highly sparse k–q-space sampling pattern with super-resolution (SR) image enhancement. Similar to the RoSA (rotating single-shot acquisition) acceleration scheme, the presented algorithm takes advantage of simultaneous k–q-space sampling procedures being able to implement directly with no hardware modifications. The method sequentially processes compressively sensed k-space’s semi-PROPELLER blades with respect to appropriately synchronized diffusion directions. The dMR image structure is expressed as a kind of minimum-spanning tree. It fades out distortions of the image’s features. Moreover, as contrasted with numerous other super-resolution algorithms, the presented method overcomes the simplifying motion model as well as blur kernel and noise estimation issues. The simulation and experimental studies have been conducted using a dMRI scanner as well as a phantom input. Combining super-resolution with time-efficient data sets resulted in a reduction of motion artifacts, improving edge delineation as well as spatial resolution.     

红外与可见光图像融合的小型实时DSP平台实现

综合利用并实时处理红外与可见光图像的互补优势信息,设计实现了一种新型的以TMS320DM642——适合视频和图像处理的高性能定点DSP为核心的嵌入式硬件平台,可编程验证多种双波段图像融合算法。叙述了系统的结构设计和工作流程,板卡各关键组成单元的功能、原理,功耗计算,高频电路设计。介绍了系统的软件流程,包括初始化程序、驱动程序和融合应用程序的实现方法。结果表明,此高速、小型、低功耗的硬件平台可应用于便携式独立工作的双波段图像融合装置中。     

基于BIRCH聚类加速的彩色图像增强算法

针对现存的大多图像增强算法增强的图像可见性丢失问题,提出了一种基于BIRCH聚类加速的彩色图像增强算法;首先,通过BIRCH聚类加速确定数据库中与输入图像直方图相似度最高的图像来提取图像特征;然后,选择最小欧氏距离的特征值进行图像融合以获取目标图像;最后,增强图像通过目标图像直方图规范化和后期处理获得;大量图像融合实验结果验证了算法的有效性,该算法扩展了图像增强的类别,解决了增强过程中可能出现的可见性丢失问题,使图像增强的适应性更强;另外,EM、CII和SSIM评估指标的结果表明该算法明显改善了增强效果。     

基于神经网络的低照度彩色图像增强算法

由于低照度彩色图像存在整体亮度低、对比度低、颜色偏暗和信噪比低等特点,所以经典图像增强算法对其增强效果非常有限。提出了一种利用BP神经网络进行彩色图像增强的算法,并将RGB图像转换成HSI图像,以保证增强处理不引起图像的色彩失真。实验证明:该方法显著地改善了低照度彩色图像的视觉效果,提高了图像整体亮度和图像的信噪比,可调节图像的动态范围,能增强图像的对比度和细节,可增加图像信息熵。     

Cellular neural networks for welding arc thermograms segmentation

Machine vision systems are used in many areas for monitoring of technological processes. Among this processes welding takes important place, where often infrared cameras are used. Besides reliable hardware, successful application of vision systems requires suitable software based on proper algorithms. One of most important group of image processing algorithms is connected to image segmentation. Obtainment of exact boundary of an object that changes shape in time, such as the welding arc, represented on a thermogram is not a trivial task. In the paper a segmentation method using supervised approach based on a cellular neural networks is presented. Simulated annealing and genetic algorithm were used for training of the network (template optimization). Comparison of proposed method to a well elaborated segmentation method based on region growing approach was made. Obtained results prove that the cellular neural network can be a valuable tool for infrared welding pool images segmentation.     

Heterogeneous platform-aware workload feature recognition for edge intelligence

With the advent of the Internet of Everything, the combination of AI (Artificial Intelligence) and edge computing has become a new research hotspot, and edge intelligence has emerged, which enables network edge devices to analyze data through AI algorithms. Since the edge computing environment is more complex and variable than cloud computing, there are many issues in building edge intelligence, such as lack of quantitative evaluation criteria, heterogeneous computing platforms, complex network topologies, and changing user requirements. To analyze the performance of edge intelligence workloads running on heterogeneous hardware platform, we target machine learning workloads in edge intelligence and analyze the impact of algorithm model complexity, edge data characteristics and heterogeneous platform differences in edge intelligence in terms of relative performance. By analyzing the machine learning workload in edge intelligence, we find that the inference time and memory usage of a model can be predicted based on the amount of computation and number of parameters of the model. Moreover, image complexity, edge data network features, and batch size all affect the performance of edge intelligence workloads. Furthermore, the upper limit of model performance on the same computing platform is limited by hardware resources. And finally, the model performance of a platform depends on its own computing power and bandwidth.