基于自适应频域滤波的红外弱小目标检测技术

研究复杂背景下弱小目标检测问题对提高靶场光电设备探测能力具有重要意义.根据红外图像的背景复杂程度,提出一种自适应高斯高通滤波算法.该算法利用改进中值滤波器对图像进行降噪,采用图像方差加权熵,定量描述红外图像背景复杂程度,根据图像背景复杂程度自动调整滤波器截止频率,实现不同背景下红外弱小目标自动检测,并利用靶场实测光电图像对算法进行了验证.实验结果表明该算法能够有效地在不同图像背景下检测到弱小目标.     

自适应双边滤波红外弱小目标检测方法

针对红外弱小目标检测,提出一种基于自适应双边滤波的背景预测算法.该算法利用空域低通滤波和图像灰度信息的非线性组合,自适应的对背景进行预测,达到提高弱小目标检测性能的目的.仿真和实验表明:与小波滤波的检测算法相比,该算法能够更加有效地从结构化背景中检测目标抑制背景.     

自适应双边滤波红外弱小目标检测方法

针对红外弱小目标检测,提出一种基于自适应双边滤波的背景预测算法.该算法利用空域低通滤波和图像灰度信息的非线性组合,自适应的对背景进行预测,达到提高弱小目标检测性能的目的.仿真和实验表明：与小波滤波的检测算法相比,该算法能够更加有效地从结构化背景中检测目标抑制背景.     

基于核各向异性扩散的红外小目标检测

为了减少红外图像中背景边缘对检测的影响,提出了一种具有鲁棒性的弱小目标检测算法,该算法利用核各向异性扩散模型进行背景预测,再与原图像差分实现弱小目标检测。为了提高算法的自适应能力,提出了一种鲁棒性扩散系数,能够根据图像背景的起伏程度自适应调整扩散系数曲线的陡峭程度。实验结果表明,与现有的检测算法相比,该算法能够在不同类型的复杂背景下有效抑制背景及其边缘,保留目标大小,降低虚警率,具有更强的鲁棒性。     

一种基于背景预测及小波变换的红外弱小目标检测方法

提出了一种新的红外弱小目标检测方法,在对红外图像进行背景预测的基础上,对残差图像采用小波变换方法增加对弱小目标的检测率,有效地提高了检测算法对低信噪比红外图像中弱小目标的检测性能。通过实测的星图数据与传统方法进行了对比和分析,证明了该方法适用于非平稳背景中低信噪比目标的检测。     

基于多特征融合的复杂背景下弱小多目标检测和跟踪算法

针对红外弱小多目标的检测和跟踪难题,提出一种基于多特征融合的复杂背景下弱小多目标检测和跟踪算法.融合红外弱小运动目标的灰度特征、梯度特征、运动特征等多个典型特性,进行复杂背景下弱小多目标的检测和跟踪.实验证明:该算法应用于复杂背景下低信噪比的红外弱小多目标图像序列能得到较理想的结果,算法检测概率高、检测速度快、具有较强鲁棒性.     

基于相关滤波器的红外弱小目标检测算法EI北大核心CSCD

针对复杂背景下红外图像中低信噪比弱小目标实时检测问题,提出一种基于相关滤波器的红外弱小目标检测算法。该算法将红外目标检测转化为模式分类问题,在离线训练阶段,利用二维高斯模型构造红外小目标训练集,在此基础上训练得到对目标背景具有区分能力的相关滤波器,在线检测阶段,利用滤波器对图像分块进行滤波操作,目标和背景的滤波响应有着显著的差异,最后生成整幅图像的滤波响应置信图以此来判断图像中是否包含目标及其具体位置。在单帧单目标图像、序列图像多目标检测实验结果表明,与经典检测算法相比,所提方法不仅具有更高检测性能,有效降低了虚警概率,而且具有较好的实时性,适用于复杂背景条件下弱小目标的实时检测。     

基于相关滤波器的红外弱小目标检测算法

针对复杂背景下红外图像中低信噪比弱小目标实时检测问题,提出一种基于相关滤波器的红外弱小目标检测算法。该算法将红外目标检测转化为模式分类问题,在离线训练阶段,利用二维高斯模型构造红外小目标训练集,在此基础上训练得到对目标背景具有区分能力的相关滤波器,在线检测阶段,利用滤波器对图像分块进行滤波操作,目标和背景的滤波响应有着显著的差异,最后生成整幅图像的滤波响应置信图以此来判断图像中是否包含目标及其具体位置。在单帧单目标图像、序列图像多目标检测实验结果表明,与经典检测算法相比,所提方法不仅具有更高检测性能,有效降低了虚警概率,而且具有较好的实时性,适用于复杂背景条件下弱小目标的实时检测。     

基于非参数背景抑制的弱小目标管线法检测

介绍了一种红外图像背景抑制的非参数方法(E_kernel)。提出了一种弱小目标的管线检测算法。E_kernel方法不同于传统的线性或非线性背景预测,它对背景杂波分布的统计特性不敏感,受其影响较小,具有非参数特性。管线检测算法对序列图像做若干相同的顺序处理,采用并行分布式计算,处理时间短。仿真试验表明,该算法能有效地检测出低信噪比红外序列图像中的弱小目标的运动轨迹,具有较高的实时性。     

利用红外夜视的海上弱小目标检测与分割

红外夜视检测技术能加强海上预警探测系统的安全性,但红外夜视仪所采集的海上红外图像存在大量“背景杂波”,严重干扰弱小目标的准确检测。针对此问题,结合主动轮廓模型与插值滤波器提出一种用于海上安全系统的红外夜视弱小目标检测算法。该算法先分别采用全局主动轮廓与局部主动轮廓搜索感兴趣目标区域,通过双层主动轮廓模型消除噪声与背景杂波对目标检测的影响。然后提出变化方向的插值滤波器沿着感兴趣区域的边缘进行滤波,缓解弱边缘对目标真实边缘的干扰。在真实的红外夜视海上图像集上对该算法进行了实验与分析,结果表明该算法能改善海上弱小目标的检测性能,对提高海上航行的安全性具有积极意义。     

Prediction of the performance of an algorithm for the detection of small targets in infrared images

Detection of small targets in infrared (IR) images is important in IR image processing. For the prediction of performance of a detection algorithm, it is necessary to calculate the probability of detection and probability of false alarm. A method is developed to calculate the probabilities in this paper. The detection is divided into two parts: the first part, which is called pre-detection, is to find out candidates for targets in a single frame of an image; and the second part is to localize the target in multiple frames of the image. Under some assumptions, the pre-detection probability, the false detection probability of single frame, detection probability and false alarm probability are derived. The algorithm for the detection of small target in IR image, which is used for the derivation of the probabilities, is contrast threshold detection based on background prediction, and a pipeline filter is used for multiframe image processing. The results show the relationship of the probabilities to the contrast of target to background, SNR, and contrast threshold.     

Edge directional 2D LMS filter for infrared small target detection

In this paper, we introduce an edge directional 2D least mean squares (LMSs) filter for small target detection in infrared (IR) images. Generally, the 2D LMS filter functions as a background prediction to apply to IR small target detection field. In order to accurately predict background objects as well as regions covered by small targets, the proposed 2D LMS filter take full advantage of edge information of prediction pixels corresponding to surrounding blocks around current filter window. And, to adjust adaptively its step size in the background and small target region, the adaptive region-dependent nonlinear step size is calculated by using the variance of the prediction pixels of the surrounding blocks. This prediction structure and adaptive step size of the proposed 2D LMS filter is applied to the background region including objects such as cloud edge and small target region differently. Through this way, the proposed 2D LMS filter predicts the background excluding small targets. Then, by subtracting the predicted background from the original IR image, small targets can be extracted. Experimental results show that the proposed 2D LMS filter has stronger target extraction and better background suppression ability compared to the existing 2D LMS filters.     

Small Target Detection Algorithm Based on Average Absolute Difference Maximum and Background Forecast

Detecting small targets in clutter scene and low SNR (Signal Noise Ratio) is an important and challenging problem in infrared (IR) images. In order to solve this problem, we should do works from two sides: enhancing targets and suppressing background. Firstly, in this paper, the system utilizes the average absolute difference maximum (AADM) as the dissimilarity measurement between targets and background region to enhance targets. Secondly, it uses a predictor to suppress the background clutter. Finally, our approach extracts the interested small target with segment threshold. Experimental results show that the algorithm proposed has better performance with respect to probability of detection and less computation complexity. It is an effective small infrared target detection algorithm against complex background.     

Infrared small target enhancement based on variance difference

In surveillance and early warning systems, the enhancement of targets is a very important stage for the high reliability detection and tracking in Infrared images with complex backgrounds. In order to enhance small targets in an Infrared image and suppress the background clutter, consequently increasing the contrast between them, this paper proposes a method using a model for the target area with a three-layer patch-image model and based on the difference between the variance of the layers in the neighboring areas of the investigated pixel. Results of the experiments indicate that the proposed method is quite effective on the enhancement of small targets as well as suppression of the background clutter in IR images with a minimum false alarm rate. This is realized while the runtime of the proposed method is minimal compared to other commonly used methods, which makes it effective to be used in real time applications.     

Dim small targets detection based on self-adaptive caliber temporal-spatial filtering

To boost the detect ability of dim small targets, this paper began by using improved anisotropy for background prediction (IABP), followed by target enhancement by improved high-order cumulates (HQS). Finally, on the basis of image pre-processing, to address the problem of missed and wrong detection caused by fixed caliber of traditional pipeline filtering, this paper used targets’ multi-frame movement correlation in the time-space domain, combined with the scale-space theory, to propose a temporal-spatial filtering algorithm which allows the caliber to make self-adaptive changes according to the changes of the targets’ scale, effectively solving the detection-related issues brought by unchanged caliber and decreased/increased size of the targets. Experiments showed that the improved anisotropic background predication could be loyal to the true background of the original image to the maximum extent, presenting a superior overall performance to other background prediction methods; the improved HQS significantly increased the signal-noise ratio of images; when the signal-noise ratio was lower than 2.6 dB, this detection algorithm could effectively eliminate noise and detect targets. For the algorithm, the lowest signal-to-noise ratio of the detectable target is 0.37.     

强杂波背景红外弱小目标检测算法

针对强杂波背景远距离红外弱小信号目标的特点,提出了一种基于自适应滤波的红外弱小信号检测方法。算法首先对图像进行消噪声处理,其次运用自适应滤波方式消除背景增强目标信号,最后进行基于点源目标(试验采集)成像信号特性的判决法则删除虚假目标,算法有效解决了光电探测设备高检测概率与低虚警率的矛盾。实验结果表明:该方法能够在单帧图像上有效提取出小区域信噪比为4的弱小信号目标,检测概率不低于0.75,虚警率不高于1次/100帧。     

Effective and robust infrared small target detection with the fusion of polydirectional first order derivative images under facet model

The robust detection of IR small target acts as one of the key techniques in the infrared search and tracking system (IRSTS). This paper presents a new method of small-target detection which formulates the problem as the detection of Gaussian-like spot. Initially, the amendatory first-order directional derivative (AFODD) based on facet model is applied to get the polydirectional derivative IR images, and the direction information of targets is reserved in these images. Then, the AFODD images are fused together to ensure the robustness and effectiveness of target detection. At last, the Principal Component Analysis (PCA) method is carried out to make targets in the fusion image more prominent, so that they can be extracted out by a simple threshold segmentation. Experiment results show that the presented method performs well even in the IR images with complex backgrounds.     

层次卷积滤波红外弱小目标检测方法

针对单帧复杂背景红外图像点目标检测算法存在复杂背景下处理效果不理想、处理时间长的问题,提出了一种层次卷积滤波检测算法。主要分为两个部分：第一,根据红外小目标特性,设计一种层次卷积滤波的算子,对图像进行滤波处理,实现图像中小目标的增效和背景抑制的效果;第二,采用基于最大值的自适应阈值方法,对图像进行二值化操作,过滤背景杂波,最终提取到待检测的目标。在大量不同背景红外图像中进行实验,论文算法在背景抑制因子和信噪比增益的性能量化结果上优于现有5种典型红外弱小目标检测算法的性能结果,且平均处理时间仅为高斯拉普拉斯（Laplacian of Gaussian,LoG）滤波算法的30.42%。通过实验对比,表明该层次卷积滤波算法可以有效解决在不同复杂背景下的红外图像中对小目标检测的问题。     

INFRARED SMALL TARGET DETECTION ALGORITHM BASED ON SELF-ADAPTIVE BACKGROUND FORECAST

A background forecast filter is presented to detect a small target under an infrared (IR) nature scene. By calculating the correlation of image pixels, the background around the small target could be forecasted. Subtracting the forecast background from original scene, the small targets would become outstanding. Experimental results show that the algorithm proposed has better performance with respect to probability of detection and less computation complexity.