background clutter rejection 背景杂波抑制
background clutter suppression 背景杂波抑制
sea area ir background clutter 海域红外背景杂波
Clutter Background 复杂背景
cloud clutter background 云杂波背景
undulant clutter background 起伏背景
ground clutter background analog 地杂波背景模拟
Firstly,DTCWT is adopted to decompose the input infrared image,which extracts multi-scale detail features of images. Then,according to difference between targets and background clutter signals,the singular value decomposition is introduced to process sub-bands,and maximum eigenvalues are utilized to compose the sub-bands.
首先采用DTCWT对图像进行正变换,获得图像的多尺度和方向细节特征;然后根据目标和背景杂波信号系数在不同尺度之间的差异,对各子带采用奇异值分解进行处理,并利用最大的特征值重构子带;最后将系数调整后的各子带逆变换到图像域,从而将弱小目标和背景杂波分离,达到抑制背景的目的。
参考来源 - 基于多尺度截断的弱小目标复杂背景抑制The dim target usually hides in the constructed background clutter and heavy noise environment. In recent years, researchers pay more and more attention on the detection and tracking of dim target in IR image sequence.
远距离的红外成像目标通常隐藏在高度结构化的背景杂波和强噪声环境中,近些年来,强杂波条件下的可见光和红外小目标的检测研究工作已愈来愈为人们所重视。
参考来源 - 红外序列图象弱小运动目标检测新方法研究Constant False Alarm Rate (CFAR) processing of SAR images facilitates target detection in spatially varying background clutter.
恒虚警（CFAR）处理是适用于不同杂波背景的目标检测的一项重要技术。
参考来源 - 合成孔径雷达图像恒虚警目标检测·2,447,543篇论文数据，部分数据来源于NoteExpress
According to the statistic characteristics of IR background clutter, an improved spatial matched filter (SMF) is presented.
针对红外背景杂波的统计特性，提出了一种改进型的空间匹配滤波算法。
The effective detection for point target in low SNR IR images is dependent greatly on the suppression of background clutter.
低信噪比条件下点目标的检测性能在很大程度上依赖于对红外背景杂波的抑制情况。
The targets are in the background clutter which intensity is heavy. So the performance of the general algorithms is not very good.
由于目标处于海天线附近的高强度背景杂波中，因此，采用常规的点目标检测算法对其进行检测的结果都不够理想。
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