基于FPGA的红外图像双向次序滤波模块的设计

针对红外图像中的物体边缘信息特点和噪声特点,提出一种基于统计排序的双向次序滤波算法,在去噪、保持图像边缘信息及小目标之间找到最佳的平衡点。给出了该算法的FPGA快速实现方法,开发的双向次序滤波FPGA IP模块已应用于多款红外产品中,能有效滤除红外图像中的椒盐类噪声,减轻非均匀性校正后的固定残差和信号处理电路的电子噪声,应用结果表明提出的算法滤波除噪能力强,图像细节损失少,设计的FPGA IP模块延时少,时序性能稳定。     

双密度复小波与系数相关性的红外图像去噪北大核心CSCD

为了去除红外图像中高斯噪声的同时,更好地保持和恢复图像的纹理边缘和细节特征,提出了基于双密度复小波与系数相关性的红外图像去噪方法。该方法充分利用双密度双树复小波在图像处理上的优势:图像信息的平移不变性、图像纹理细节的多方向选择性等,基于对图像小波系数分布的假设,根据当前小波系数与其父、子小波系数的相关性,对无噪的小波系数作贝叶斯估计,以恢复无噪的红外图像,最后对去噪图像进行引导滤波,去除图像的波纹效果。实验数据显示,该方法在EPI和FSIM以及图像的视觉效果上优于部分现有算法,证明该方法在噪声去除、纹理边缘的保持和恢复上具有更好的性能。     

一种快速的图像边缘精确提取算法

图像边缘检测是图像处理与分析中最基础的内容之一。现有的边缘检测算法存在检测精度低、抗噪性能差、处理速度慢等缺点。针对这些问题,提出了一种快速图像边缘检测算法。通过基于图像边缘信息的阈值分割,能快速地提取出图像的边缘轮廓,通过对其进行中心细化,可达到对物体边缘精确定位的目的。仿真实验证明,该算法在滤除图像噪声、保留细节边缘、细化边缘宽度和保持边缘连通性方面都获得了不错的效果,并且运算量小,既适用于对视频图像的实时处理,也适用于对图像中的隐蔽目标特征的提取和分析。     

X光图像中缺陷的自动提取方法研究

针对炭素制品X光图像的特点,对其缺陷的提取技术进行了研究,提出了基于迭代的阈值构造方法和基于数学形态学的边缘提取算法。为快速准确地提取缺陷,设计了目标边界提取算法和基于小波变换的图像增强算法,实现了原始图像中目标区域的增强及其背景的去除。在此基础上,为排除噪声干扰的影响,采用数学形态学和迭代阈值分割相结合的方法从目标区域中提取出缺陷区域,并在迭代阈值分割的基础上,利用基于数学形态学的边缘提取算法提取了缺陷的边缘。实验结果表明,该法很好地实现了缺陷区域及其边缘的自动提取,且受噪声影响很小,为进一步的缺陷特征参量的提取与选择奠定了良好的基础。     

基于平稳小波变换的有噪弱目标检测方法的研究

利用平稳小波变换的多尺度边缘检测算法分别对模糊图像、低对比度图像和加入噪声的图像进行了边缘检测,验证了多尺度二次B样条小波的检测效果,也比较了三种小波局部模极大值方法在抗噪性、计算量及检测效果等方面的性能,并且针对对比度低,受噪声污染严重的目标图像,提出一种能够根据不同背景计算出自适应阈值的新方法,使其在抗噪的同时又能较好地提取出微弱目标边缘。实验证明,利用多尺度二次B样条小波边缘检测算法能有效地排除噪声干扰,准确地提取出微弱边缘,可以实现3%对比度下的有噪图像的目标探测问题。     

一种新的各向异性扩散红外噪声实时抑制算法

提出一种基于各向异性扩散偏微分方程的红外图像噪声抑制算法.通过将形态学处理和红外图像局部特征相结合,建立了一种新的扩散系数.该系数利用形态学膨胀腐蚀操作获取梯度算子,改善了Perona-Malik(P-M)梯度算子对噪声的敏感性,实现了均匀区域扩散增强且边缘细节区域扩散减弱的目的.算法已在EVM-DM642硬件平台上实时运行,实验表明:它在有效平滑噪声的同时较好的保持了图像边缘细节信息.     

零件矩形边界框区域自动提取算法及应用

为了提高图像处理速度,提出了基于机器视觉的目标图像矩形边界框区域提取算法。分析了图像在含有孤立点噪声的情况下该算法的可靠性以及阈值选取对实验结果的影响;设计出了针对齿形链板和自动扶梯用链板图像的抗噪模板;利用最小二乘法和最小区域法确定链板圆孔几何参数,并从目标与背景之比和算法复杂度两个方面分析了该算法对处理速度的贡献能力。实验表明,该算法对于孤立点噪声具有良好的抑制作用,可快速、准确地找出包含目标图像的矩形区域。     

基于改进CLAHE的SF_6红外图像增强

针对六氟化硫(SF_6)红外图像对比度低、纹理细节模糊而难以增强泄漏区域的问题,提出了基于改进限制对比度的自适应直方图均衡化(CLAHE)的SF_6红外图像增强算法。采用双边滤波将原始图像分为基础图像和细节图像;采用CLAHE算法来处理基础图像,提高泄漏区域的对比度;对细节图像进行分段线性变换和拉普拉斯变换图像,以突出图像的边缘;将两幅图像进行线性叠加以获取最终的红外图像,实现图像增强。实验结果表明,算法对SF_6红外图像泄漏区域的增强效果优于常见的几种红外图像增强算法,不仅有效地抑制了噪声和提高泄漏区域的对比度,而且突出泄漏区域的边缘,丰富了细节信息。     

多尺度形态算子融合图像滤波技术及滤波质量评价

针对舰载红外警戒系统的红外和电视图像,提出了一种新的海空背景下受强杂波、噪声污染的图像目标滤波算法和滤波效果的定量评价算子。算法采用多尺度的形态算子对输入的图像并行滤波,大尺度形态算子抑制图像噪声,小尺度形态算子提取目标边缘细节信息。处理后的图像进行基于树状小波帧变换的图像信息融合,融合图像可完备提取不同尺度滤波后的图像信息。针对目标检测跟踪的图像滤波算法的评价,提出了目标与背景的交叉分辨力评价算子及评价准则。仿真实验表明。该滤波算法要优于中值滤波、自适应滤波、小波变换滤波算法,滤波质量的定量评价算法是合理的、有效的。算法适用于舰载红外警戒系统。     

基于复小波与最大后验估计的红外图像滤波

针对现有的红外图像去噪算法在边缘恢复和保持上的缺陷,提出了基于双树复小波与最大后验估计的红外图像去噪方法。充分利用双树复小波变换的多分辨率分析、平移不变性和多方向选择性等优秀特性,对含噪的红外图像作双树复小波变换;基于对高斯噪声和无噪图像的概率密度分布的假设,在小波域中对无噪图像的小波系数作最大后验估计,实现红外图像的去噪和恢复。红外图像去噪实验证明了方法的有效性,算法在有效去除噪声的同时,对边缘细节的保持和恢复较理想,去噪的图像质量指标PSNR和SSIM比现有的方法分别提高1dB和2%以上。     

基于多特征和FCM的图像边缘检测方法

提出了一种新的基于多特征和FCM的边缘检测算法.该方法根据边缘点附近灰度分布特点构造了多个反映边缘特性的特征分量,并利用输入图像提取该组特征分量,组成一个反映图像边缘特征的数据集.用FCM聚类算法将该数据集分为两类,即边缘点数据和非边缘点数据,实现边缘检测.该方法无需确定阈值,对弱边缘检测较敏感,在特征的选取上充分考虑了边缘和噪声的本质区别,因而具有优异的抗噪性能.     

Infrared small target tracking based on sample constrained particle filtering and sparse representation

Infrared search and track technology for small target plays an important role in infrared warning and guidance. In view of the tacking randomness and uncertainty caused by background clutter and noise interference, a robust tracking method for infrared small target based on sample constrained particle filtering and sparse representation is proposed in this paper. Firstly, to distinguish the normal region and interference region in target sub-blocks, we introduce a binary support vector, and combine it with the target sparse representation model, after which a particle filtering observation model based on sparse reconstruction error differences between sample targets is developed. Secondly, we utilize saliency extraction to obtain the high frequency area in infrared image, and make it as a priori knowledge of the transition probability model to limit the particle filtering sampling process. Lastly, the tracking result is brought about via target state estimation and the Bayesian posteriori probability calculation. Theoretical analyses and experimental results show that our method can enhance the state estimation ability of stochastic particles, improve the sparse representation adaptabilities for infrared small targets, and optimize the tracking accuracy for infrared small moving targets.     

基于局部熵的图像过渡区边缘检测算法

提出了一种新的红外目标边缘检测算法,利用局部熵值分析法和过渡区提取相结合的方法,通过Top-hat与Bottom-hat变换,建立有效平均熵和对应灰度值的曲线关系,选择合适的阈值,在此基础上执行形态学闭合操作.仿真结果表明,该方法在去除不必要的细节的同时,能很好地提取目标的边缘信息.     

复杂天空背景下的红外弱小目标跟踪

为解决传统跟踪算法不能有效区分复杂天空云层背景边缘和红外弱小目标,从而在跟踪过程中产生“偏移”的问题。在时空上下文原理基础上分析跟踪“偏移”的原因,引入高斯曲率滤波,提出一种改进的时空上下文红外弱小目标跟踪算法。该算法首先采用高斯曲率滤波对上下文区域进行预处理,在保留上下文区域背景边缘的同时剔除高频的红外弱小目标和噪声,从而获得准确的红外弱小目标置信图,利用红外弱小目标置信图估计出红外弱小目标位置。采用四组复杂天空背景下的红外弱小目标图像序列进行实验,并与经典的模板匹配算法、基于粒子滤波的均值漂移算法和快速压缩跟踪算法三种跟踪算法作比较。实验结果表明,算法在主观视觉和客观评价指标方面均优于其他三种算法,具有更高的目标跟踪精度与较好的实时性,可以实现对复杂天空背景下红外弱小目标的有效跟踪。     

Morphological center operator for enhancing small target obtained by infrared imaging sensor

Infrared small target in image obtained by infrared imaging sensor is usually different from the surrounding regions, which is the important region in infrared image for different applications. However, the infrared small target is dim and embedded in clutter background, which makes the small target difficult to be detected or recognized. So, it is important to enhance infrared small target. Morphological center operator could smooth important image features, which may be well used for infrared small target enhancement. To well enhance infrared small target, a morphological center operator based method is proposed. The morphological center operator is specified to suppress most of the background of the infrared image through the strategy of feature extraction. Then, the infrared small target is well enhanced. Experimental results show that, the proposed method performs well for enhancing infrared small target and the performance is better than some other methods. So, the proposed method could further improve the performance of infrared imaging sensor.     

基于形态学的复杂背景目标检测算法的研究

复杂背景下目标检测存在诸多困难,主要为背景对目标检测的干扰,大量噪声存在导致传统导数边缘检测方法的失效等。针对上述两点,提出了分割区域图像、利用形态学方法检测目标的新算法。首先利用目标与背景灰度差异性来确定目标的大致区域,将其分割出来,然后再结合多结构元素法进行目标的精确检测。通过与原图像分割、聚类算法分割实验比较,该算法具有较好的抗干扰性和抗噪性能。     

The infrared and visible image fusion algorithm based on target separation and sparse representation

Although the fused image of the infrared and visible image takes advantage of their complementary, the artifact of infrared targets and vague edges seriously interfere the fusion effect. To solve these problems, a fusion method based on infrared target extraction and sparse representation is proposed. Firstly, the infrared target is detected and separated from the background rely on the regional statistical properties. Secondly, DENCLUE (the kernel density estimation clustering method) is used to classify the source images into the target region and the background region, and the infrared target region is accurately located in the infrared image. Then the background regions of the source images are trained by Kernel Singular Value Decomposition (KSVD) dictionary to get their sparse representation, the details information is retained and the background noise is suppressed. Finally, fusion rules are built to select the fusion coefficients of two regions and coefficients are reconstructed to get the fused image. The fused image based on the proposed method not only contains a clear outline of the infrared target, but also has rich detail information.     

Spatio-temporal segmentation of moving objects using edge features in infrared videos

In this paper, a novel method is proposed for spatio-temporal segmentation of moving objects using edge features in infrared videos. We define motion saliency of edge (MSoE) to generate the MSoE-map. The seeds of moving objects are extracted from the MSoE-map by using Otsu's method and subsequently compensated by historical data. An improved layer-based region growing method is applied to the seeds to achieve spatial segmentation of moving objects. The region growing method has an adjustable growing threshold. So, one of the focuses of our work is how to determine the best growing threshold. A Markov Random Field (MRF) based criterion with maximum a posterior (MAP) estimation principle is proposed for performance evaluation of moving object segmentation without ground truth (GT) in infrared videos. This criterion can be considered as an object function of threshold determination during global searching. The global optimum is accomplished by using simulated annealing (SA) algorithm to obtain the best growing threshold. The final segmentation mask of moving objects is grown from the seeds with the best growing threshold. Experimental results are provided to illustrate that the proposed method has better performance for moving object segmentation with fewer effects of object-background misclassification in infrared videos.     

一种基于形状的红外图像泄漏气体检测方法

针对工业生产中泄漏气体导致的爆炸和火灾问题, 提出一种基于形状和SVM分类的红外图像泄漏气体检测方法。采用泄漏气体和干扰物红外图像样本的形状特征训练SVM分类器, 通过对红外图像序列采用基于背景差分的运动检测得到候选目标区域, 再对候选目标区域提取其形状特征, 最后使用SVM分类器进行判别, 从而得到最终的检测结果。使用乙烯气体泄漏仿真数据进行实验, 检测率最高可达98%, 结果表明, 采用该方法可以有效检测泄漏气体, 相比其他方法, 极大地减少了干扰物造成的误检。