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自适应双边滤波红外弱小目标检测方法 总被引:1,自引:0,他引:1
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. 相似文献
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为了在有效地检测复杂场景下红外弱小目标的同时保持较低虚警率,在满足算法实现实时性的前提下,提出一种基于引导滤波和分块自适应阈值的单帧红外弱小目标检测。首先,为缓解边缘杂波干扰,采用具有保边特性的引导滤波对图像进行背景估计;然后,利用弱小目标具备的局部灰度最大特性,提出基于软阈值非极大值抑制的九宫格滤波计算目标的概率。通过加权的方式进一步剔除背景,抑制结果中不满足目标特性的区域;最后,针对复杂场景目标检测虚警率和漏检率高的问题,提出一种分块自适应阈值分割方法提取候选目标。实验结果表明,在公开数据集上与Top-Hat、LCM和Max-Median等经典方法相比,所提方法性能优于其他方法,恒虚警下不同复杂度场景的召回率分别达到87.97%、84.93%和86.22%,可有效抑制背景,增强目标信号,提高红外弱小目标检测的召回率,且具有更好的场景鲁棒性。 相似文献
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针对复杂云背景下的弱小目标探测,提出了一种基于光流估计和自适应背景抑制相结合的弱小目标检测算法.首先根据红外图像中云的移动规律,对云背景下的红外图像进行光流分析,提取运动云区.在光流场的计算中结合了云运动的特点以及光流方程的两个约束条件,对传统的基于梯度的光流法予以改进.同时发现移动云区对目标探测的影响较大,为了抑制移动云区对弱小目标的干扰,提出了自适应抑制复杂背景的算法,在光流场分析提取的移动云区中,利用代表背景复杂程度的背景因子,自适应调整分割阈值,抑制复杂背景的干扰.这样只在容易引起虚警的移动云区进行背景抑制处理,简化了计算量,降低了云区对弱小目标的干扰,减少了虚警和误判.实验结果表明该算法可以显著减少云区造成的虚警,并且能够探测出弱小目标. 相似文献
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为了构建鲁棒的背景模型和提高前景目标检测的准确性, 综合考虑同一位置的像素点在时间上的关联性和与其相邻像素的空间关联性, 基于经典的ViBe算法中的随机聚类思想提出了一种复杂背景建模和前景检测方法. 利用样本一致性原理, 采用前n帧序列图像得到初始化背景, 避免了Ghost现象的发生; 根据实际复杂背景的动态反馈获取自适应聚类阈值和自适应更新阈值进行随机聚类, 从而实现了对动态背景的适应性; 通过全局扰动阈值和局部像素级判断阈值的结合, 实现了对光照缓慢变化、快速变化以及突然变化的免疫性, 准确地分割前景目标. 对多组数据集的测试结果表明, 本文算法较大地提高了背景模型对动态背景、光照变化及相机抖动的复杂背景的适应性和鲁棒性. 算法还能很好地适用于红外图像检测运动目标的场合, 扩展了本算法的应用范围. 在没有进行任何图像预处理和形态学后处理情况下, 得到的原始前景检测精度优于其他对比算法. 相似文献
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In this Letter, the color constancy and its realization were studied and a novel color constancy image enhancement algorithm under poor illumination was presented. The purpose of this algorithm is to maintain the hue of an image during the processing so that the change of saturation can be minimized. The original image was first multiplied by a scale parameter obtained by the adaptive quadratic function to enhance the luminance, and then the edge details were restored by a shifting parameter. Numerical results of the Simon Fraser University (SFU) image database indicated that the proposed algorithm performed much better in preserving the hue and saturation and avoiding color distortion compared with the existing image enhancement algorithms. 相似文献
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A tensor diffusion level set method is presented to extract infrared (IR) targets contour under a sky-mountain-water complex background. The proposed model combines tensor diffusion operator and the eigenvalues of tensor-image into a common energy minimization level set framework. By incorporating the information of image tensor diffusion operator into the external energy term, the level set function can move in a specific way. And eigenvalues of tensor-image are used for the regularization of zero level curves in order to diminish the influence of image ‘clutter’ and noise. An additional benefit of the proposed method is robust to initial conditions. Experimental results show very good performance of the tensor diffusion level set method for IR targets contours extraction. 相似文献
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The traditional iterative reconstruction algorithms of positron emission tomography cannot effectively suppress the noise in low SNR case. Recently anisotropic diffusion (AD) is introduced into tomography reconstruction, which can improve the reconstructed image. Although AD reconstruction algorithm can suppress noise, it does not perverse the detail edge information accurately, especially the thin edges. In order to solve the problem, we introduce a new anisotropic diffusion term, which can preserve the detail edges effectively, into the maximum likelihood algorithm, and combine with median filter, forming the regularized maximum likelihood algorithm in PET image reconstruction (PML_NewAD). Results of computer simulated demonstrate that compared with the other classical reconstruction algorithms, PML_NewAD not only availably suppress the noise and produce a higher quality image, but also preserve the structure of image's edge excellently. 相似文献
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针对红外图像弱小目标检测技术中复杂背景杂波干扰问题,提出了一种基于波原子变换的红外图像背景抑制算法。首先,采用波原子变换对图像进行多尺度和多方向分解,获得原始图像的多尺度和多方向细节特征;然后,根据目标和背景杂波信号的差异,通过频域变换设计的系数调整函数修正经波原子变换后各子带系数,再经波原子逆变换重构得到估计的背景图像;最后,将其与原始图像相减获得背景杂波抑制后的图像。用真实的红外图像序列进行实验,结果显示,与最大中值和小波变换两种算法相比,该算法能有效地抑制红外弱小目标复杂背景杂波,突出目标信号,提高信杂比,具有良好的背景抑制性能。 相似文献
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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. 相似文献
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In this paper, an ensemble template algorithm is proposed to extract targets from blurred infrared images. First, the image pixels are divided according to their gray values into three pixel sets, a target set, a background set and the third set without class label. Second, the neighborhood statistical characteristics for each pixel are calculated as its template features. Third, ensemble detectors are designed using target pixels and background pixels based on their template features, and these ensemble detectors are used to detect the third pixel set. To evaluate the performance of the proposed extraction algorithm, this paper compares the ensemble template with other extraction algorithms using blurred infrared image of hand trace. Experimental results show that the ensemble template algorithm proposed in this paper exhibits better extraction performance. 相似文献
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The algorithm of maximum variance between clusters (traditional Otsu algorithm) is discussed, and its advantage is given also. In order to segment the PCB photoelectric image better, on the basis of the traditional Otsu algorithm, considering the different influence of image segmentation about the factors of the distance between target and background as well as each kind of cohesion, an improved Otsu algorithm is proposed, and its basic principle and segmentation advantages are analyzed in detail. In order to evaluate these segmentation results impersonally by using different algorithms, the quantitative criteria of gray-level contrast and district interior uniformity are adopted to evaluate these segmentation results impersonally. Finally, the different segmentation experiment contrasts of PCB photoelectric image between our algorithm and other algorithms is executed, the results of experiment indicate that our algorithm has relatively better segmentation quality. 相似文献
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A new contrast enhancement algorithm for image is proposed employing wavelet neural network (WNN)and stationary wavelet transform (SWT). Incomplete Beta transform (IBT) is used to enhance the global contrast for image. In order to avoid the expensive time for traditional contrast enhancement algorithms,which search optimal gray transform parameters in the whole gray transform parameter space, a new criterion is proposed with gray level histogram. Contrast type for original image is determined employing the new criterion. Gray transform parameter space is given respectively according to different contrast types,which shrinks the parameter space greatly. Nonlinear transform parameters are searched by simulated annealing algorithm (SA) so as to obtain optimal gray transform parameters. Thus the searching direction and selection of initial values of simulated annealing is guided by the new parameter space. In order to calculate IBT in the whole image, a kind of WNN is proposed to approximate the IBT. Having enhanced the global contrast to input image, discrete SWT is done to the image which has been processed by previous global enhancement method, local contrast enhancement is implemented by a kind of nonlinear operator in the high frequency sub-band images of each decomposition level respectively. Experimental results show that the new algorithm is able to adaptively enhance the global contrast for the original image while it also extrudes the detail of the targets in the original image well. The computation complexity for the new algorithm is O(MN) log(MN), where M and N are width and height of the original image, respectively. 相似文献